{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import re\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>kc</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16740</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16807</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16811</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16813</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16818</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s_id         sub_time  prob_id              kc  correct  difficulty\n",
       "0    85  2016/8/23 11:13    16740  el_tb_w_7au1.1        1           1\n",
       "1    85  2016/8/23 11:13    16807  el_tb_w_7au1.1        1           1\n",
       "2    85  2016/8/23 11:13    16811  el_tb_w_7au1.1        1           1\n",
       "3    85  2016/8/23 11:13    16813  el_tb_w_7au1.1        1           1\n",
       "4    85  2016/8/23 11:13    16818  el_tb_w_7au1.1        1           1"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names=['s_id','sub_time','prob_id','kc','correct','difficulty']\n",
    "data=pd.read_table('~/data/yi/classba_eng1.csv', sep=',',header=0, encoding='ISO-8859-1',names=names)\n",
    "data2=pd.read_table('~/data/yi/classba_eng2.csv', sep=',',header=0, encoding='ISO-8859-1',names=names)\n",
    "data.head()\n",
    "data2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>kc</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>342277</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79370</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342278</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:06</td>\n",
       "      <td>79372</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342279</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79374</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342280</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79377</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342281</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:08</td>\n",
       "      <td>79380</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         s_id        sub_time  prob_id           kc  correct  difficulty\n",
       "342277  18709  2017/3/2 23:05    79370  egv_cc_9u11        1           2\n",
       "342278  18709  2017/3/2 23:06    79372  egv_cc_9u11        0           2\n",
       "342279  18709  2017/3/2 23:07    79374  egv_cc_9u11        0           2\n",
       "342280  18709  2017/3/2 23:07    79377  egv_cc_9u11        0           2\n",
       "342281  18709  2017/3/2 23:08    79380  egv_cc_9u11        0           2"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=[data,data2]\n",
    "final=pd.concat([data,data2], axis=0, ignore_index=True)\n",
    "final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#len(final.kc.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#final.groupby(['s_id','kc']).count()#.sort_values(by='s_id')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#final.kc.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>kc</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "      <th>kc_reduce</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16740</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16807</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16811</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16813</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16818</td>\n",
       "      <td>el_tb_w_7au1.1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16823</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16826</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16827</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16829</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16830</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16855</td>\n",
       "      <td>el_tb_w_7au1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16857</td>\n",
       "      <td>el_tb_w_7au1.3</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16859</td>\n",
       "      <td>el_tb_w_7au1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16860</td>\n",
       "      <td>el_tb_w_7au1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16861</td>\n",
       "      <td>el_tb_w_7au1.3</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16894</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16897</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16899</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16900</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16901</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16942</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16943</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16944</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16945</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16946</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16947</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16949</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16950</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16952</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16953</td>\n",
       "      <td>el_tb_w_7au1.2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>el</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342252</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:50</td>\n",
       "      <td>79326</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342253</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:51</td>\n",
       "      <td>79328</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342254</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:52</td>\n",
       "      <td>79330</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342255</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:52</td>\n",
       "      <td>79331</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342256</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79333</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342257</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79334</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342258</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79336</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342259</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:55</td>\n",
       "      <td>79337</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342260</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:55</td>\n",
       "      <td>79339</td>\n",
       "      <td>ev_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>ev</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342261</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:57</td>\n",
       "      <td>79340</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342262</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:57</td>\n",
       "      <td>79343</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342263</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:58</td>\n",
       "      <td>79344</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342264</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:59</td>\n",
       "      <td>79346</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342265</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:59</td>\n",
       "      <td>79350</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342266</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:00</td>\n",
       "      <td>79352</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342267</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:00</td>\n",
       "      <td>79355</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342268</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79356</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342269</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79359</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342270</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79361</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342271</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:02</td>\n",
       "      <td>79363</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342272</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:02</td>\n",
       "      <td>79364</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342273</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:04</td>\n",
       "      <td>79366</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342274</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79368</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342275</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79369</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342276</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79369</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342277</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79370</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342278</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:06</td>\n",
       "      <td>79372</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342279</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79374</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342280</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79377</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342281</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:08</td>\n",
       "      <td>79380</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>egv</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342282 rows × 7 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         s_id         sub_time  prob_id              kc  correct  difficulty  \\\n",
       "0          85  2016/8/23 11:13    16740  el_tb_w_7au1.1        1           1   \n",
       "1          85  2016/8/23 11:13    16807  el_tb_w_7au1.1        1           1   \n",
       "2          85  2016/8/23 11:13    16811  el_tb_w_7au1.1        1           1   \n",
       "3          85  2016/8/23 11:13    16813  el_tb_w_7au1.1        1           1   \n",
       "4          85  2016/8/23 11:13    16818  el_tb_w_7au1.1        1           1   \n",
       "5          85  2016/8/23 11:13    16823  el_tb_w_7au1.2        1           1   \n",
       "6          85  2016/8/23 11:13    16826  el_tb_w_7au1.2        1           1   \n",
       "7          85  2016/8/23 11:13    16827  el_tb_w_7au1.2        1           1   \n",
       "8          85  2016/8/23 11:13    16829  el_tb_w_7au1.2        1           1   \n",
       "9          85  2016/8/23 11:13    16830  el_tb_w_7au1.2        1           1   \n",
       "10         85  2016/8/23 11:15    16855  el_tb_w_7au1.3        1           1   \n",
       "11         85  2016/8/23 11:15    16857  el_tb_w_7au1.3        1           1   \n",
       "12         85  2016/8/23 11:15    16859  el_tb_w_7au1.3        0           1   \n",
       "13         85  2016/8/23 11:15    16860  el_tb_w_7au1.3        0           1   \n",
       "14         85  2016/8/23 11:15    16861  el_tb_w_7au1.3        0           1   \n",
       "15         85  2016/8/23 11:16    16894  el_tb_w_7au1.2        1           1   \n",
       "16         85  2016/8/23 11:16    16897  el_tb_w_7au1.2        1           1   \n",
       "17         85  2016/8/23 11:16    16899  el_tb_w_7au1.2        1           1   \n",
       "18         85  2016/8/23 11:16    16900  el_tb_w_7au1.2        1           1   \n",
       "19         85  2016/8/23 11:16    16901  el_tb_w_7au1.2        1           1   \n",
       "20         85  2016/8/23 11:17    16942  el_tb_w_7au1.2        1           1   \n",
       "21         85  2016/8/23 11:17    16943  el_tb_w_7au1.2        1           1   \n",
       "22         85  2016/8/23 11:17    16944  el_tb_w_7au1.2        1           1   \n",
       "23         85  2016/8/23 11:17    16945  el_tb_w_7au1.2        1           1   \n",
       "24         85  2016/8/23 11:17    16946  el_tb_w_7au1.2        1           1   \n",
       "25         85  2016/8/23 11:17    16947  el_tb_w_7au1.2        1           1   \n",
       "26         85  2016/8/23 11:17    16949  el_tb_w_7au1.2        1           1   \n",
       "27         85  2016/8/23 11:17    16950  el_tb_w_7au1.2        1           1   \n",
       "28         85  2016/8/23 11:17    16952  el_tb_w_7au1.2        1           1   \n",
       "29         85  2016/8/23 11:17    16953  el_tb_w_7au1.2        1           1   \n",
       "...       ...              ...      ...             ...      ...         ...   \n",
       "342252  18709   2017/3/2 22:50    79326      ev_cc_9u11        1           2   \n",
       "342253  18709   2017/3/2 22:51    79328      ev_cc_9u11        0           2   \n",
       "342254  18709   2017/3/2 22:52    79330      ev_cc_9u11        0           2   \n",
       "342255  18709   2017/3/2 22:52    79331      ev_cc_9u11        1           2   \n",
       "342256  18709   2017/3/2 22:53    79333      ev_cc_9u11        0           2   \n",
       "342257  18709   2017/3/2 22:53    79334      ev_cc_9u11        1           2   \n",
       "342258  18709   2017/3/2 22:53    79336      ev_cc_9u11        1           2   \n",
       "342259  18709   2017/3/2 22:55    79337      ev_cc_9u11        0           2   \n",
       "342260  18709   2017/3/2 22:55    79339      ev_cc_9u11        0           2   \n",
       "342261  18709   2017/3/2 22:57    79340     egv_cc_9u11        1           2   \n",
       "342262  18709   2017/3/2 22:57    79343     egv_cc_9u11        0           2   \n",
       "342263  18709   2017/3/2 22:58    79344     egv_cc_9u11        1           2   \n",
       "342264  18709   2017/3/2 22:59    79346     egv_cc_9u11        1           2   \n",
       "342265  18709   2017/3/2 22:59    79350     egv_cc_9u11        1           2   \n",
       "342266  18709   2017/3/2 23:00    79352     egv_cc_9u11        0           2   \n",
       "342267  18709   2017/3/2 23:00    79355     egv_cc_9u11        1           2   \n",
       "342268  18709   2017/3/2 23:01    79356     egv_cc_9u11        0           2   \n",
       "342269  18709   2017/3/2 23:01    79359     egv_cc_9u11        1           2   \n",
       "342270  18709   2017/3/2 23:01    79361     egv_cc_9u11        0           2   \n",
       "342271  18709   2017/3/2 23:02    79363     egv_cc_9u11        0           2   \n",
       "342272  18709   2017/3/2 23:02    79364     egv_cc_9u11        0           2   \n",
       "342273  18709   2017/3/2 23:04    79366     egv_cc_9u11        0           2   \n",
       "342274  18709   2017/3/2 23:05    79368     egv_cc_9u11        0           2   \n",
       "342275  18709   2017/3/2 23:05    79369     egv_cc_9u11        0           2   \n",
       "342276  18709   2017/3/2 23:05    79369     egv_cc_9u11        0           2   \n",
       "342277  18709   2017/3/2 23:05    79370     egv_cc_9u11        1           2   \n",
       "342278  18709   2017/3/2 23:06    79372     egv_cc_9u11        0           2   \n",
       "342279  18709   2017/3/2 23:07    79374     egv_cc_9u11        0           2   \n",
       "342280  18709   2017/3/2 23:07    79377     egv_cc_9u11        0           2   \n",
       "342281  18709   2017/3/2 23:08    79380     egv_cc_9u11        0           2   \n",
       "\n",
       "       kc_reduce  \n",
       "0             el  \n",
       "1             el  \n",
       "2             el  \n",
       "3             el  \n",
       "4             el  \n",
       "5             el  \n",
       "6             el  \n",
       "7             el  \n",
       "8             el  \n",
       "9             el  \n",
       "10            el  \n",
       "11            el  \n",
       "12            el  \n",
       "13            el  \n",
       "14            el  \n",
       "15            el  \n",
       "16            el  \n",
       "17            el  \n",
       "18            el  \n",
       "19            el  \n",
       "20            el  \n",
       "21            el  \n",
       "22            el  \n",
       "23            el  \n",
       "24            el  \n",
       "25            el  \n",
       "26            el  \n",
       "27            el  \n",
       "28            el  \n",
       "29            el  \n",
       "...          ...  \n",
       "342252        ev  \n",
       "342253        ev  \n",
       "342254        ev  \n",
       "342255        ev  \n",
       "342256        ev  \n",
       "342257        ev  \n",
       "342258        ev  \n",
       "342259        ev  \n",
       "342260        ev  \n",
       "342261       egv  \n",
       "342262       egv  \n",
       "342263       egv  \n",
       "342264       egv  \n",
       "342265       egv  \n",
       "342266       egv  \n",
       "342267       egv  \n",
       "342268       egv  \n",
       "342269       egv  \n",
       "342270       egv  \n",
       "342271       egv  \n",
       "342272       egv  \n",
       "342273       egv  \n",
       "342274       egv  \n",
       "342275       egv  \n",
       "342276       egv  \n",
       "342277       egv  \n",
       "342278       egv  \n",
       "342279       egv  \n",
       "342280       egv  \n",
       "342281       egv  \n",
       "\n",
       "[342282 rows x 7 columns]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def simp(x):\n",
    "    return re.split('_',str(x))[0]\n",
    "final['kc_reduce']=final['kc'].map(lambda x: simp(x))\n",
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final.to_csv('/Users/zhenyuezhu/data/yi/english_details.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['el', 'eg', 'ev', 'ec', 'er', 'ew', 'egv', 'zk', 'st', 'cc', 'hj',\n",
       "       'cj', 'zt', 'ezh', 'ez', 'nan'], dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.kc_reduce.unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>s_id</th>\n",
       "      <th>kc</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">85</th>\n",
       "      <th>ec</th>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "      <td>170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "      <td>200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "      <td>240</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "      <td>224</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>195</th>\n",
       "      <th>eg</th>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">200</th>\n",
       "      <th>ec</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>201</th>\n",
       "      <th>eg</th>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">202</th>\n",
       "      <th>eg</th>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "      <td>92</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">203</th>\n",
       "      <th>ec</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "      <td>135</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">204</th>\n",
       "      <th>ec</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "      <td>140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">206</th>\n",
       "      <th>ec</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "      <td>113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">207</th>\n",
       "      <th>ec</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>el</th>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">208</th>\n",
       "      <th>ec</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">18700</th>\n",
       "      <th>er</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ew</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18701</th>\n",
       "      <th>ec</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>egv</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18702</th>\n",
       "      <th>ec</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>egv</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18703</th>\n",
       "      <th>ec</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "      <td>49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>egv</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18704</th>\n",
       "      <th>eg</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18705</th>\n",
       "      <th>eg</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18706</th>\n",
       "      <th>ec</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "      <td>42</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>egv</th>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">18709</th>\n",
       "      <th>ec</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>eg</th>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "      <td>55</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>egv</th>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>er</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>ev</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3375 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "           sub_time  prob_id  correct  difficulty\n",
       "s_id  kc                                         \n",
       "85    ec        170      170      170         170\n",
       "      eg        200      200      200         200\n",
       "      el        240      240      240         240\n",
       "      er         80       80       80          80\n",
       "      ev        224      224      224         224\n",
       "      ew          2        2        2           2\n",
       "195   eg         12       12       12          12\n",
       "200   ec        120      120      120         120\n",
       "      eg         60       60       60          60\n",
       "      el        140      140      140         140\n",
       "201   eg          4        4        4           4\n",
       "202   eg         32       32       32          32\n",
       "      el         70       70       70          70\n",
       "      er         30       30       30          30\n",
       "      ev         92       92       92          92\n",
       "      ew         30       30       30          30\n",
       "203   ec         60       60       60          60\n",
       "      eg         35       35       35          35\n",
       "      el        135      135      135         135\n",
       "204   ec         60       60       60          60\n",
       "      eg         30       30       30          30\n",
       "      el        140      140      140         140\n",
       "206   ec         60       60       60          60\n",
       "      eg         45       45       45          45\n",
       "      el        113      113      113         113\n",
       "207   ec         60       60       60          60\n",
       "      eg         30       30       30          30\n",
       "      el         70       70       70          70\n",
       "208   ec         60       60       60          60\n",
       "      eg         24       24       24          24\n",
       "...             ...      ...      ...         ...\n",
       "18700 er         25       25       25          25\n",
       "      ev         25       25       25          25\n",
       "      ew          1        1        1           1\n",
       "18701 ec         40       40       40          40\n",
       "      eg         58       58       58          58\n",
       "      egv        41       41       41          41\n",
       "      er         40       40       40          40\n",
       "      ev         20       20       20          20\n",
       "18702 ec         40       40       40          40\n",
       "      eg         58       58       58          58\n",
       "      egv        40       40       40          40\n",
       "      er         35       35       35          35\n",
       "      ev         20       20       20          20\n",
       "18703 ec         20       20       20          20\n",
       "      eg         49       49       49          49\n",
       "      egv        21       21       21          21\n",
       "      er         10       10       10          10\n",
       "      ev         10       10       10          10\n",
       "18704 eg         42       42       42          42\n",
       "18705 eg         42       42       42          42\n",
       "18706 ec         10       10       10          10\n",
       "      eg         42       42       42          42\n",
       "      egv        21       21       21          21\n",
       "      er          5        5        5           5\n",
       "      ev         10       10       10          10\n",
       "18709 ec         20       20       20          20\n",
       "      eg         55       55       55          55\n",
       "      egv        41       41       41          41\n",
       "      er          5        5        5           5\n",
       "      ev         20       20       20          20\n",
       "\n",
       "[3375 rows x 4 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.groupby(['s_id','kc']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "el 54705\n",
      "eg 71457\n",
      "ev 100128\n",
      "ec 31255\n",
      "er 27718\n",
      "ew 5396\n",
      "egv 15694\n",
      "zk 1076\n",
      "st 11561\n",
      "cc 3945\n",
      "hj 12453\n",
      "cj 4384\n",
      "zt 2393\n",
      "ezh 100\n",
      "ez 15\n",
      "nan 2\n"
     ]
    }
   ],
   "source": [
    "for i in final.kc.unique():\n",
    "    print(i, len(final.loc[final['kc']==i]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "final.to_csv('/Users/zhenyuezhu/data/yi/english.csv', sep=',')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>kc</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16740</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16807</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16811</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16813</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16818</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16823</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16826</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16827</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16829</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:13</td>\n",
       "      <td>16830</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16855</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16857</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16859</td>\n",
       "      <td>el</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16860</td>\n",
       "      <td>el</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:15</td>\n",
       "      <td>16861</td>\n",
       "      <td>el</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16894</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16897</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16899</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16900</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:16</td>\n",
       "      <td>16901</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16942</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16943</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16944</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16945</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16946</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16947</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16949</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16950</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16952</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>85</td>\n",
       "      <td>2016/8/23 11:17</td>\n",
       "      <td>16953</td>\n",
       "      <td>el</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342252</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:50</td>\n",
       "      <td>79326</td>\n",
       "      <td>ev</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342253</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:51</td>\n",
       "      <td>79328</td>\n",
       "      <td>ev</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342254</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:52</td>\n",
       "      <td>79330</td>\n",
       "      <td>ev</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342255</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:52</td>\n",
       "      <td>79331</td>\n",
       "      <td>ev</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342256</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79333</td>\n",
       "      <td>ev</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342257</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79334</td>\n",
       "      <td>ev</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342258</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:53</td>\n",
       "      <td>79336</td>\n",
       "      <td>ev</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342259</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:55</td>\n",
       "      <td>79337</td>\n",
       "      <td>ev</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342260</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:55</td>\n",
       "      <td>79339</td>\n",
       "      <td>ev</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342261</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:57</td>\n",
       "      <td>79340</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342262</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:57</td>\n",
       "      <td>79343</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342263</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:58</td>\n",
       "      <td>79344</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342264</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:59</td>\n",
       "      <td>79346</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342265</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 22:59</td>\n",
       "      <td>79350</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342266</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:00</td>\n",
       "      <td>79352</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342267</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:00</td>\n",
       "      <td>79355</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342268</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79356</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342269</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79359</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342270</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:01</td>\n",
       "      <td>79361</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342271</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:02</td>\n",
       "      <td>79363</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342272</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:02</td>\n",
       "      <td>79364</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342273</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:04</td>\n",
       "      <td>79366</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342274</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79368</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342275</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79369</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342276</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79369</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342277</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79370</td>\n",
       "      <td>egv</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342278</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:06</td>\n",
       "      <td>79372</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342279</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79374</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342280</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79377</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342281</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:08</td>\n",
       "      <td>79380</td>\n",
       "      <td>egv</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>342282 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         s_id         sub_time  prob_id   kc  correct  difficulty\n",
       "0          85  2016/8/23 11:13    16740   el        1           1\n",
       "1          85  2016/8/23 11:13    16807   el        1           1\n",
       "2          85  2016/8/23 11:13    16811   el        1           1\n",
       "3          85  2016/8/23 11:13    16813   el        1           1\n",
       "4          85  2016/8/23 11:13    16818   el        1           1\n",
       "5          85  2016/8/23 11:13    16823   el        1           1\n",
       "6          85  2016/8/23 11:13    16826   el        1           1\n",
       "7          85  2016/8/23 11:13    16827   el        1           1\n",
       "8          85  2016/8/23 11:13    16829   el        1           1\n",
       "9          85  2016/8/23 11:13    16830   el        1           1\n",
       "10         85  2016/8/23 11:15    16855   el        1           1\n",
       "11         85  2016/8/23 11:15    16857   el        1           1\n",
       "12         85  2016/8/23 11:15    16859   el        0           1\n",
       "13         85  2016/8/23 11:15    16860   el        0           1\n",
       "14         85  2016/8/23 11:15    16861   el        0           1\n",
       "15         85  2016/8/23 11:16    16894   el        1           1\n",
       "16         85  2016/8/23 11:16    16897   el        1           1\n",
       "17         85  2016/8/23 11:16    16899   el        1           1\n",
       "18         85  2016/8/23 11:16    16900   el        1           1\n",
       "19         85  2016/8/23 11:16    16901   el        1           1\n",
       "20         85  2016/8/23 11:17    16942   el        1           1\n",
       "21         85  2016/8/23 11:17    16943   el        1           1\n",
       "22         85  2016/8/23 11:17    16944   el        1           1\n",
       "23         85  2016/8/23 11:17    16945   el        1           1\n",
       "24         85  2016/8/23 11:17    16946   el        1           1\n",
       "25         85  2016/8/23 11:17    16947   el        1           1\n",
       "26         85  2016/8/23 11:17    16949   el        1           1\n",
       "27         85  2016/8/23 11:17    16950   el        1           1\n",
       "28         85  2016/8/23 11:17    16952   el        1           1\n",
       "29         85  2016/8/23 11:17    16953   el        1           1\n",
       "...       ...              ...      ...  ...      ...         ...\n",
       "342252  18709   2017/3/2 22:50    79326   ev        1           2\n",
       "342253  18709   2017/3/2 22:51    79328   ev        0           2\n",
       "342254  18709   2017/3/2 22:52    79330   ev        0           2\n",
       "342255  18709   2017/3/2 22:52    79331   ev        1           2\n",
       "342256  18709   2017/3/2 22:53    79333   ev        0           2\n",
       "342257  18709   2017/3/2 22:53    79334   ev        1           2\n",
       "342258  18709   2017/3/2 22:53    79336   ev        1           2\n",
       "342259  18709   2017/3/2 22:55    79337   ev        0           2\n",
       "342260  18709   2017/3/2 22:55    79339   ev        0           2\n",
       "342261  18709   2017/3/2 22:57    79340  egv        1           2\n",
       "342262  18709   2017/3/2 22:57    79343  egv        0           2\n",
       "342263  18709   2017/3/2 22:58    79344  egv        1           2\n",
       "342264  18709   2017/3/2 22:59    79346  egv        1           2\n",
       "342265  18709   2017/3/2 22:59    79350  egv        1           2\n",
       "342266  18709   2017/3/2 23:00    79352  egv        0           2\n",
       "342267  18709   2017/3/2 23:00    79355  egv        1           2\n",
       "342268  18709   2017/3/2 23:01    79356  egv        0           2\n",
       "342269  18709   2017/3/2 23:01    79359  egv        1           2\n",
       "342270  18709   2017/3/2 23:01    79361  egv        0           2\n",
       "342271  18709   2017/3/2 23:02    79363  egv        0           2\n",
       "342272  18709   2017/3/2 23:02    79364  egv        0           2\n",
       "342273  18709   2017/3/2 23:04    79366  egv        0           2\n",
       "342274  18709   2017/3/2 23:05    79368  egv        0           2\n",
       "342275  18709   2017/3/2 23:05    79369  egv        0           2\n",
       "342276  18709   2017/3/2 23:05    79369  egv        0           2\n",
       "342277  18709   2017/3/2 23:05    79370  egv        1           2\n",
       "342278  18709   2017/3/2 23:06    79372  egv        0           2\n",
       "342279  18709   2017/3/2 23:07    79374  egv        0           2\n",
       "342280  18709   2017/3/2 23:07    79377  egv        0           2\n",
       "342281  18709   2017/3/2 23:08    79380  egv        0           2\n",
       "\n",
       "[342282 rows x 6 columns]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>kc</th>\n",
       "      <th>s_id</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"23\" valign=\"top\">cc</th>\n",
       "      <th>5441</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5448</th>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5449</th>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "      <td>193</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5467</th>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "      <td>95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5477</th>\n",
       "      <td>458</td>\n",
       "      <td>458</td>\n",
       "      <td>458</td>\n",
       "      <td>458</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5478</th>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "      <td>203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5479</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5481</th>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "      <td>428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5482</th>\n",
       "      <td>285</td>\n",
       "      <td>285</td>\n",
       "      <td>285</td>\n",
       "      <td>285</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5486</th>\n",
       "      <td>323</td>\n",
       "      <td>323</td>\n",
       "      <td>323</td>\n",
       "      <td>323</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5500</th>\n",
       "      <td>430</td>\n",
       "      <td>430</td>\n",
       "      <td>430</td>\n",
       "      <td>430</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5507</th>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "      <td>402</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5529</th>\n",
       "      <td>310</td>\n",
       "      <td>310</td>\n",
       "      <td>310</td>\n",
       "      <td>310</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5558</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5562</th>\n",
       "      <td>404</td>\n",
       "      <td>404</td>\n",
       "      <td>404</td>\n",
       "      <td>404</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5566</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5567</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5569</th>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5576</th>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5600</th>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "      <td>88</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5695</th>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5855</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"7\" valign=\"top\">cj</th>\n",
       "      <th>5478</th>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "      <td>152</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5806</th>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5807</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5808</th>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "      <td>120</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5810</th>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "      <td>315</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5909</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">st</th>\n",
       "      <th>6292</th>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6299</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">zk</th>\n",
       "      <th>1197</th>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1316</th>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "      <td>250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1317</th>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "      <td>94</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4372</th>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4377</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5441</th>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "      <td>112</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5495</th>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5776</th>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5783</th>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "      <td>84</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5808</th>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5810</th>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "      <td>107</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5919</th>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "      <td>56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5976</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6069</th>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "      <td>62</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"14\" valign=\"top\">zt</th>\n",
       "      <th>5493</th>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5765</th>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5766</th>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "      <td>123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5767</th>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "      <td>48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5768</th>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5769</th>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "      <td>137</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5774</th>\n",
       "      <td>485</td>\n",
       "      <td>485</td>\n",
       "      <td>485</td>\n",
       "      <td>485</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5775</th>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "      <td>307</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5782</th>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "      <td>386</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5786</th>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "      <td>338</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5808</th>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "      <td>58</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5810</th>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5919</th>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "      <td>289</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6171</th>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "      <td>51</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3375 rows × 4 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         sub_time  prob_id  correct  difficulty\n",
       "kc s_id                                        \n",
       "cc 5441         6        6        6           6\n",
       "   5448        60       60       60          60\n",
       "   5449       193      193      193         193\n",
       "   5467        95       95       95          95\n",
       "   5477       458      458      458         458\n",
       "   5478       203      203      203         203\n",
       "   5479        33       33       33          33\n",
       "   5481       428      428      428         428\n",
       "   5482       285      285      285         285\n",
       "   5486       323      323      323         323\n",
       "   5495        33       33       33          33\n",
       "   5500       430      430      430         430\n",
       "   5507       402      402      402         402\n",
       "   5529       310      310      310         310\n",
       "   5558         1        1        1           1\n",
       "   5562       404      404      404         404\n",
       "   5566        16       16       16          16\n",
       "   5567        27       27       27          27\n",
       "   5569        11       11       11          11\n",
       "   5576        86       86       86          86\n",
       "   5600        88       88       88          88\n",
       "   5695        23       23       23          23\n",
       "   5855        30       30       30          30\n",
       "cj 5478         1        1        1           1\n",
       "   5495       152      152      152         152\n",
       "   5806        72       72       72          72\n",
       "   5807        18       18       18          18\n",
       "   5808       120      120      120         120\n",
       "   5810       315      315      315         315\n",
       "   5909        27       27       27          27\n",
       "...           ...      ...      ...         ...\n",
       "st 6292        16       16       16          16\n",
       "   6299        20       20       20          20\n",
       "zk 1197        68       68       68          68\n",
       "   1316       250      250      250         250\n",
       "   1317        94       94       94          94\n",
       "   4372        28       28       28          28\n",
       "   4377        84       84       84          84\n",
       "   5441       112      112      112         112\n",
       "   5495         2        2        2           2\n",
       "   5776        56       56       56          56\n",
       "   5783        84       84       84          84\n",
       "   5808        25       25       25          25\n",
       "   5810       107      107      107         107\n",
       "   5919        56       56       56          56\n",
       "   5976        48       48       48          48\n",
       "   6069        62       62       62          62\n",
       "zt 5493        30       30       30          30\n",
       "   5765        40       40       40          40\n",
       "   5766       123      123      123         123\n",
       "   5767        48       48       48          48\n",
       "   5768        75       75       75          75\n",
       "   5769       137      137      137         137\n",
       "   5774       485      485      485         485\n",
       "   5775       307      307      307         307\n",
       "   5782       386      386      386         386\n",
       "   5786       338      338      338         338\n",
       "   5808        58       58       58          58\n",
       "   5810        26       26       26          26\n",
       "   5919       289      289      289         289\n",
       "   6171        51       51       51          51\n",
       "\n",
       "[3375 rows x 4 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.groupby(['kc', 's_id']).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "PL0, Pt, Pg, Ps\n",
    "0.54, 0.01, 0.29, 0.09 -- SSE 3528.19 (grid search)\n",
    "0.56, 0.01, 0.29, 0.09 -- SSE 3528.21 (grid search)\n",
    "0.58, 0.01, 0.29, 0.09 -- SSE 3528.48 (grid search)\n",
    "\n",
    "0.517, 0.00255, 0.434, 0.205 -- SSE 3296.7 (gradient descent)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "res0=np.zeros(15)\n",
    "res1=np.ones(15)\n",
    "\n",
    "l0 = 0.5\n",
    "trans = 0.2\n",
    "guess = 0.3\n",
    "slip = 0.2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def plot(prev, pred, upper, C1, C0):\n",
    "    plt.axis([0, 20, 0, 1.01])\n",
    "    line1, = plt.plot(prev, '.r-', label='Pl_t')\n",
    "    line2, = plt.plot(pred, 'hk-', label='Pc_t')\n",
    "    line3, = plt.plot(upper, 'om-', label='upper_t')\n",
    "    \n",
    "    line4, = plt.plot(C1, 'xb-', label='Pc_1')\n",
    "    line5, =plt.plot(C0, '*g-', label='Pc_0')\n",
    "    plt.legend(handles=[line1, line2, line3, line4, line5])    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def similarity_stop_policy(l0):\n",
    "    eps=0.01\n",
    "    delta=0.95\n",
    "    \n",
    "    pc1=(1-slip)*l0+guess*(1-l0)\n",
    "    total=0\n",
    "    \n",
    "    if pc1>0:\n",
    "        ul0= l0*(1-slip)/(l0*(1-slip)+(1-l0)*guess)\n",
    "        pl1= ul0+(1-ul0)*trans\n",
    "        p_cc= (1-slip)*pl1+guess*(1-pl1)\n",
    "        \n",
    "        if abs(pc1-p_cc)<eps:\n",
    "            total+=pc1\n",
    "\n",
    "    if pc1<1:\n",
    "        ul0= l0*slip/(l0*slip+(1-l0)*(1-guess))\n",
    "        pl1= ul0+(1-ul0)*trans\n",
    "        p_cbc= (1-slip)*pl1+guess*(1-pl1)\n",
    "\n",
    "        if abs(pc1-p_cbc)<eps:\n",
    "            total+=(1-pc1)\n",
    "    \n",
    "    return total > delta"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def stability_stop_policy(l0,prev_c1,prev_c0):\n",
    "    eps=0.01\n",
    "    pc1=(1-slip)*l0+guess*(1-l0)\n",
    "    \n",
    "    if pc1>0:\n",
    "        ul0= l0*(1-slip)/(l0*(1-slip)+(1-l0)*guess)\n",
    "        pl1= ul0+(1-ul0)*trans\n",
    "        p_cc= (1-slip)*pl1+guess*(1-pl1)\n",
    "        \n",
    "    if pc1<1:\n",
    "        ul0= l0*slip/(l0*slip+(1-l0)*(1-guess))\n",
    "        pl1= ul0+(1-ul0)*trans\n",
    "        p_cbc= (1-slip)*pl1+guess*(1-pl1)\n",
    "    \n",
    "#    print(np.abs(p_cc-prev_c1), np.abs(p_cbc-prev_c0))\n",
    "# should change \"or\" to \"and\"     \n",
    "    return np.abs(p_cc-prev_c1)<eps and np.abs(p_cbc-prev_c0)<eps"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "def process(response, l0, trans, guess, slip):\n",
    "    C1=[]\n",
    "    C0=[]\n",
    "    prev=[]\n",
    "    pred=[]\n",
    "    \n",
    "    prev_c1=0\n",
    "    prev_c0=0\n",
    "    find_sim=1\n",
    "    find_sta=1\n",
    "    \n",
    "    all_correct=np.ones(len(res1))\n",
    "    upper=[]\n",
    "    \n",
    "    for i in list(range(len(response))):\n",
    "        prev.append(l0)       \n",
    "        pc1=(1-slip)*l0+guess*(1-l0)\n",
    "        pred.append(pc1)\n",
    "        \n",
    "\n",
    "        if (i==0):\n",
    "            up0=l0\n",
    "            \n",
    "        up0= up0*(1-slip)/(up0*(1-slip)+(1-up0)*guess)            \n",
    "        up_t= up0+(1-up0)*trans\n",
    "        u_cc= (1-slip)*up_t+guess*(1-up_t)\n",
    "        upper.append(u_cc) \n",
    "#        print(i, up0, up_t, u_cc)\n",
    "#        print(u_cc, pc1)\n",
    "        up0=up_t\n",
    "\n",
    "        \n",
    "        if pc1>0:\n",
    "            ul0= l0*(1-slip)/(l0*(1-slip)+(1-l0)*guess)\n",
    "            pl1_t= ul0+(1-ul0)*trans\n",
    "            p_cc= (1-slip)*pl1_t+guess*(1-pl1_t)\n",
    "#            print(i, ul0, pl1_t, p_cc)\n",
    "            C1.append(p_cc)\n",
    "\n",
    "        if pc1<1:\n",
    "            ul0= l0*slip/(l0*slip+(1-l0)*(1-guess))\n",
    "            pl1_f= ul0+(1-ul0)*trans\n",
    "            p_cbc= (1-slip)*pl1_f+guess*(1-pl1_f)\n",
    "            C0.append(p_cbc)\n",
    "\n",
    "        if (response[i]==0):\n",
    "            l0=pl1_f\n",
    "        if (response[i]==1):\n",
    "            l0=pl1_t\n",
    "            \n",
    "        stop_sim=similarity_stop_policy(l0) \n",
    "        if stop_sim and find_sim:\n",
    "            print('similarity stop at here', i+1,'th problem')\n",
    "            find_sim=0\n",
    "\n",
    "        stop_sta=stability_stop_policy(l0, prev_c1, prev_c0) \n",
    "        if stop_sta and find_sta:\n",
    "            print('stability stop at here', i+1,'th problem', np.abs(u_cc-pc1))\n",
    "            if np.abs(u_cc-pc1)<0.01:\n",
    "                print ('++ condition is met')\n",
    "            find_sta=0\n",
    "\n",
    "        prev_c1=p_cc\n",
    "        prev_c0=p_cbc\n",
    "        \n",
    "        \n",
    "    return [prev, pred, upper, C1, C0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "stability stop at here 5 th problem 0.356345992437\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzt3Xl8FPX9+PHXJ5ubhAQSRBDCIQgKBJAzKpe2CmrRKnjA\nF6xQkba2oq0VTFFUsKhUrSeiUhXFAzzgZ6XWgkCVy6iRcohyHwLGCAkk5Nz374/ZhE2ym2yS3ewk\n+3762MfuXO95Z1zeM/uZmc8YEUEppVRoCQt2AkoppRqeFn+llApBWvyVUioEafFXSqkQpMVfKaVC\nkBZ/pZQKQVr8lVIqBGnxV0qpEFRj8TfGLDTG/GCM2eJlujHGPGmM2WmM2WyMOd//aSqllPKncB/m\neRl4GnjVy/RRQFfXaxDwnOu9WsnJydKxY0efklRKKWX54osvfhSRVvWNU2PxF5G1xpiO1cxyFfCq\nWP1EbDDGJBpj2ojI4eriduzYkYyMjFolq5RSoc4Ys88fcfzR5n8WcMBt+KBrnFJKKZtq0BO+xpgp\nxpgMY0xGVlZWQ65aKaWUG38U/0NAe7fhdq5xVYjIAhHpLyL9W7Wqd5OVUkqpOvLlhG9NlgO3GWPe\nxDrRm1NTe79SKrQVFxdz8OBBCgoKgp2KbUVHR9OuXTsiIiICEr/G4m+MeQMYDiQbYw4C9wERACIy\nH/gQuBzYCeQDNwckU6VUk3Hw4EHi4+Pp2LEjxphgp2M7IkJ2djYHDx6kU6dOAVmHL1f73FjDdAF+\n57eMlFJNXkFBgRb+ahhjSEpKIpDnRvUOX6VUUGjhr16gt48Wf6WUCkFa/JVSKgRp8VdKhSSHw0Gf\nPn3o2bMnY8eOJT8/H4C4uDivy+zdu5fFixc3VIoBpcVfKdU4rF8Pf/2r9e4HMTExZGZmsmXLFiIj\nI5k/f36NyzSl4u+P6/yVUqrupk2DzMzq58nJgc2bwemEsDBITYWEBO/z9+kDTzzhcwpDhgxh8+bN\nNc43ffp0tm/fTp8+fbjpppu44447fF6H3eiRv1LK/nJyrMIP1ntOjt9Cl5SUsGLFCnr16lXjvHPn\nzmXIkCFkZmY26sIPeuSvlAo2X47Q16+HSy6BoiKIjITXX4e0tHqt9tSpU/Tp0wewjvwnT55cr3iN\njRZ/pZT9paXBypWwejUMH17vwg+n2/xDlRZ/pVTjkJbml6JfH/Hx8Zw4cSKoOfiLtvkrpZSPUlNT\ncTgc9O7dm8cffzzY6dSLHvkrpULSyZMnazUeICIiglWrVgUqpQalR/5KKRWC9MhfKaUq+d///seE\nCRMqjIuKimLjxo1Bysj/tPgrpVQlvXr1avJXAmmzj1JKhSAt/kopFYK0+CulVAjS4q+UUiFIi79S\nyvby8vK45557aNGiBenp6eV979eHt/78ffX++++zbdu2eucRLFr8lVK2tnbtWjp06MDf//53jh8/\nzuOPP05KSgpr166tV9y69OfvrrEXf73UUykVVNOmTav2ssrt27eTnZ1dPnzq1ClOnTrF2LFjOffc\ncz0u06dPH56oY3/+r776KvPmzcMYQ2pqKosWLaoy/7p161i+fDlr1qxh9uzZvPPOO5x99tk+r88O\ntPgrpUJaWX/+I0eOZOvWrcyePZt169aRnJzMTz/95HGZCy64gNGjR3PllVcyZsyYBs7YP7T4K6WC\nqqYj9AkTJvDaa69VGX/ppZd6PCr3laf+/J9//nnGjh1LcnIyAC1btqxzfLvT4q+UsrVbbrmFFStW\nkJ+fz6lTp4iJiSE2NpZbbrmlXnFDvT9/PeGrlLK1oUOHsn//fu644w4SExO588472b9/P0OHDvX7\nui6++GKWLFlSfo7BW7MPNP6+/bX4K6VsLzY2ljlz5nDs2DFmz55NbGxsQNbTo0cP0tPTGTZsGL17\n9+bOO+/0Ou8NN9zAo48+St++fdm1a1dA8gkkbfZRSoUkb/3233TTTdx00001Ln/hhRc26ks99chf\nKaVCkB75K6VUNebMmcOSJUsqjBs7dizp6elBysg/tPgrpVQ10tPTG32h90SbfZRSKgRp8VdKqRCk\nxV8ppYLooYceCsp6fSr+xpiRxpgdxpidxpjpHqYnGGP+nzHma2PMVmPMzf5PVSmlGicRwel0epxm\n2+JvjHEAzwCjgPOAG40x51Wa7XfANhHpDQwH/maMifRzrrZy9PWjrO+4ntVhq1nfcT1HXz8asjHs\nkINdYtghBzvF8JdA5LJ371569uxZPjxv3jxmzZrF8OHDuf3228v7+t+0aRMAs2bNYsKECaSlpdG1\na1deeOGF8mUfffRRBgwYQGpqKvfdd195/G7dujFx4kR69uzJgQMHquQwffr08j6Gxo8fX++/qTZ8\nudpnILBTRHYDGGPeBK4C3O9uECDeGGOAOOAnoMTPudrG0dePsmPKDpz51p68cF8hO6bsAKD1+NZ+\niSEi4ARxClLq+uz2Lk4h6+0sdv1pF85TbjFu2UFRVhHJVydb/1dcLxGxVuw2DiBreRb77t2Hs8At\nxq93UHCogOQrk8vnc1ceC8j+Zzb7ZnlY/kABSVckVVrQ87bI/mc2+x7wEGO/hxhe2CGGHXIIaIxa\nfsf9xR//3morPz+fzMxM1q5dy6RJk9iyZQsAmzdvZsOGDeTl5dG3b1+uuOIKtmzZwnfffcemTZsQ\nEUaPHs3atWtJSUnhu+++45VXXmHw4MEe1zN37lyefvrpoPQxZNz/IXucwZgxwEgR+bVreAIwSERu\nc5snHlgOdAfigetF5J/Vxe3fv79kZGTUM/26O/r6UXan76ZwfyFRKVF0ntPZpy+SOIX1HdZTdLCo\nyjRHgoO2U9viPOXEWeA8/e72ufRUKc4CJ/k78r3vHg1eC6VSwRTVIYq0vWn1jrN9+/byvvi/m/Yd\nJzM9320LkLshFyms+g/CRBmaD27ucZm4PnF0faJrtTns3buXK6+8srywz5s3j5MnT7J69Wruvfde\nLr74YgBSUlLYvHkzTzzxBE6nkwceeACAiRMncs011/Dpp5+ydOlSEhMTAevO4RkzZnDJJZcwYsQI\n9uzZU20ecXFxXu82dt9O5X+3MV+ISP9qg/rAX9f5XwZkAhcDZwMfG2P+KyK57jMZY6YAU8DaoMHi\n7UjCWewkcWgihYcKrdfBQooOFZV/LjxUSNHhIqTYc2UuzSnl4BMHCYsOwxHjICw6jLCYMOvd9Tmy\neSRhMWHkb/X+yLgO6R3AASbMYBwGwqjwbsIMOGDn73d6jdHtH90AMMZYO5OyV6Vx28dt9xzAwHlv\nnlf+2dN0gG3XbfO8ozLQY0kPr8u52zpmq/cYSz3E8MAOMeyQQ6BjFO4v9Gl5f/JU+Ksb76vw8PAK\n7fAFBQXln61GDKoMexovIsyYMYNbb721wrS9e/fSrFmzeuUYSL4U/0NAe7fhdq5x7m4G5or1M2Kn\nMWYP1q+ATe4zicgCYAFYR/51Tbq+dt+zu7zwl3HmO9lx844q84Y1CyOqXRRRZ0WROCyRqHZRfD//\ne0qOVT1sj0qJIm2fb0dF6zuup3Bf1X9IUR2i6PRgJ59iHJh3wGuMNr9q41OM3TN2e46REsUZ151R\n4/K7UnZ5Xb7Vta18yiEqJcp7jGsaTww75NAQMfytpiP06v6t9F3dt87rbd26NT/88APZ2dnExcXx\nwQcfMHLkSADeeustRowYwaeffkpCQgIJCQkALFu2jBkzZpCXl8fq1auZO3cuMTExzJw5k/HjxxMX\nF8ehQ4eIiIjwOY+IiAiKi4trtYw/+HK1z+dAV2NMJ9dJ3Buwmnjc7QcuATDGtAa6Abv9mWh9OUuc\nHFt1jB1Td1R79NJtYTdS/53KgK0DuOj4RQw5MYRB3wyiz8o+nPvquXR+qDNdn+pKWGzFTRcWG0bn\nhzr7nE/nOZ09x5jTuGLYIQe7xLBDDnaK4S+ByiUiIoJ7772XgQMH8vOf/5zu3buXT4uOjqZv375M\nnTqVl156qXx8amoqI0aMYPDgwcycOZO2bdty6aWXMm7cONLS0ujVqxdjxoypVVfPU6ZMITU1tcFP\n+NbY5g9gjLkceAJwAAtFZI4xZiqAiMw3xrQFXgbaYP2wnysiVR+946Yh2vydJU5y1uaQtSSLrHey\nKM4qJqxZGAhVjvyhdu2ZdT1n0BRj2CEHu8SwQw52iuGNp7bsYOVS2fDhw5k3bx79+1dsVp81axZx\ncXH86U9/Csh6PQlkm79PxT8QAlX8pVQ4vua4VfDfzaL4h2LCYsNI+kUSZ4w9g5ajWvLjez9WaPMH\n60ii24JuDX4lg1KhqLbFvyGFSvFvlB27VT4K6PRgJ6LaRZH1dqWCf2USZ1xnFXxHrKN8+bIC31BH\nEkqpxmP16tUex8+aNatecQcNGkRhYcUm50WLFtGrV696xa2rRlf8PV2p883EbwDKC36rsa1Iujyp\nQsGvrPX41lrslVINZuPGjcFOoYJGV/x3p1e9UgcgPDmctL1pOJp5L/hKKaUsja5jN29X6pRkl2jh\nV0opHzWq4n/iixOebzgiMNcfK6VUU9Voiv+xVcfIHJGJo4V156y7YF1/rJRSjVWjKP5Z72SxedRm\nolKiGPj1QLq92I2oDlFgrGvz9RJNpZSqHdsX/+8XfM/W67YS3z+evmv7EnVWFK3HtyZtbxrDncNJ\n25umhV8pVWsOh6O82+axY8eSn++9vy1P1q5dy/nnn094eDhLly4NUJaBY9viLyLsm7OPb2/9lpYj\nW9L7495EtGzYvi+UUsH3yCPwyScVx33yiTW+PmJiYsjMzGTLli1ERkYyf/78Wi2fkpLCyy+/zLhx\n4+qXSJDYsviLU9g5bSd7/rKH1v/Xmp7v96z2mn2lVNM1YABcd93pHcAnn1jDAwb4bx1Dhgxh506r\nl9xXX32V1NRUevfuzYQJE7wu07FjR1JTUwkLs2UZrZHtrvN3Fjn55uZv+GHxD7Sb1o6z/3a21YWx\nUqpJmjYNanqWSdu2cNll0KYNHD4M554L999vvTzp0weeeMK39ZeUlLBixQpGjhzJ1q1bmT17NuvW\nrSM5OZmffvqpdn9MI2KrXVZpXilbrtrCD4t/oNNfO3H2Y1r4lVLQooVV+Pfvt95btKh/zLLHJ/bv\n35+UlBQmT57MqlWrGDt2LMnJyQC0bNmy/iuyKdsc+Rf/VMz/rvgfuZtyOeeFc2j767bBTkkp1QB8\nOUIva+qZOROeew7uuw9GjKjfesva/EOVLY78Cw4W8NWQrzjx1Ql6LO2hhV8pVa6s8L/9NjzwgPXu\nfg7Any6++GKWLFlCdnY2gDb7BFL+jny+uvArCg8UkvqvVFr90rcnDSmlQsPnn1sFv+xIf8QIa/jz\nz/2/rh49epCens6wYcPo3bs3d955ZzV5fU67du1YsmQJt956Kz16+PaITLsIWn/+3Uw3WXjmQj4/\nHse3jgQe/m8S8X3jg5KLUqph2bk/fzsJZH/+QT3y33SkGbMKzmXoxBgt/Eop1YCCdsJ3L824jx7c\nz1Y6fHgK0Lt0lVL2M2fOHJYsWVJh3NixY0lPTw9SRv4RtOJfRBglOPiSFnTb5/vDjpVSqiGlp6c3\n+kLvSdCafRIpwgG8RgcmhA3ihRegtDRY2SilVGgJWvE/g0IeZjNxFNOqjWHKFOjbFz7+OFgZKaVU\n6AjqCd/BHU7x8j0nuPn3ESxZAidPwqWXwhVXwLZtwcxMKaWatqC1+cf3iyctI63CuF/8Ap56CmbP\nhtRUmDIFZs2CM84ITo5KKdVUBf0mL3dRUfCnP8HOnfCb38CCBdC1Kzz8MBQUBDs7pVQwHT5xmGEv\nD+PIySN+iVff/vwLCwu5/vrr6dKlC4MGDWLv3r1+yauh2Kr4l0lOtn4BbNkCw4bB9OnQvTu89Za1\nIwhE395KKXt7cO2DfLr/Ux5Y84Bf4tW3P/+XXnqJFi1asHPnTu644w7uvvtuv+TVUGzTsZsn3bvD\n8uWwciX88Y9www1WV65z58K771q3ebv3+6GUanym/WsamUe8d7D23/3/xSnO8uHnMp7juYznCDNh\nDEkZ4nGZPmf24YmRPvbpjNWf/+bNmwGrP/958+ZhjCE1NZVFixZ5XGbZsmXMmjULgDFjxnDbbbch\nIhjTOHoitnXxL3PJJfDFF/Dqq5CeDsePw6hRcPPNsHRpxX4/lFJNy8C2A9l9bDc/nvoRpzgJM2Ek\nxyZzdouz/RK/rv35Hzp0iPbt2wMQHh5OQkIC2dnZ5d1B212jKP4ADodV7MeOhUcfhYcegvnzoVUr\nWLPGeu/RAxrJTlcp5eLLEfpvPvgNC75cQHR4NEWlRVx77rU8e8Wz9VpvWX/+YB35T548meeffz5k\n+vO3ZZt/deLiYPhwaN4cRo60fgXcfz/06mU1E6Wnw1dfQZD6q1NKBcDRvKNM7TeVDZM3MLXfVL+c\n9C1r88/MzOSpp54iMjKyVsufddZZHDhwALB+PeTk5JCUlFTvvBpKoyv+ZW38S5fCihXw0UfQsqX1\nKLj27a0TwuefD126wN13w6ZNuiNQqrF79/p3eeaKZ+h9Zm+eueIZ3r3+3YCspzb9+Y8ePZpXXnkF\ngKVLl3LxxRc3mvZ+aITF31Pf3kuXWo92+89/4MgRePFFOOcceOwxGDQIOnaEO++EdevA6bSuDNIr\nhpRSldWmP//JkyeTnZ1Nly5deOyxx5g7d24DZlp/QevPv3///pKRkRHQdRw7Zl0t9M471i+EoiLr\nQdADB1rFfulS+NnPKl4xpCeOlQo87c/fN022P/9Aa9ECbrrJ2gFkZcHrr1u/BP71L8jJsbqS6NIF\nLr8cbrwRiothz56aO5jTXw5KqcauSRd/d82bw7hx1v0BWVnWUX6PHrBrl1Xsn3oKLrsMOneG2Fjr\nfoLRo637C+bPh1Wr4MABq9lowICKzxAt++UwYIDv+egORKnGYc6cOfTp06fCa86cOcFOq/5EpMYX\nMBLYAewEpnuZZziQCWwF1tQUs1+/fhJMq1aJJCeLzJxpvS9ZIrJmjciLL4rcfbfINdeI9OwpEh0t\nYp0ytl7R0db4iy4SiYkRueIKkfh4kYcfFlm/XmTrVpEDB0Ryc0VKS2te/6pVnod98fDDVedftcoa\n31Ax7JCDXWLYIQc7xajOtm3b/BOoifO0nYAM8aFu1/TypfA7gF1AZyAS+Bo4r9I8icA2IMU1fEZN\ncYNZ/GtTeEtLRfbvF1m5UmT+fJE//lFk9GiR7t1FwsIq7hgqv4wRad5cpH17kR49RC64QGTkSJHr\nrhO55RaRsWNFYmNFfv5zkWbNRKZNE3npJZFXXxV54w2Rd94RWb5cZMUKa/1r11o7mC++ENm8WeTl\nl0VathR5802R778XWbpUJClJ5L33RLKzRY4dE8nJETlxQiQvT+TUKZHCQpHiYuvvcjrrvxPyx06s\nqcSwQw52ilGdbdu2idPp9E+wJsrpdAa0+Nd4wtcYkwbMEpHLXMMzXL8Y/uo2z2+BtiLyF19/cTTE\nCV9vHnnEaqJxP7n7ySfWlUR//rNvMcqaesaPt+48vv9+qxO6nBzIzbVeZZ8rv7t/rmVfUgFhjLW7\nCguzmrXCw62b6squWjOm6mf34dJS6++IjLROqjdrBhERp6dXXpen4aIiOHECoqOtTvyaN7fi1UZR\nkbVNY2Lg1ClISKhbjJwcq+kvPx8SE2sXo6jIuvekrsvbMcYNN1jP2fDnBRF79uwhPj6epKSkRnV5\nZEMREbKzszlx4gSdOnWqMM1fJ3x9ucP3LOCA2/BBYFClec4BIowxq4F44O8i8mp9kwsUTwV+xAjf\nv9iVrw666qrTw9df73seZXFuvhkWLrTOLQwYYJ14Li62/vHV9Lm4GJYsgQ8+sLq8uOIKq5A7nb69\nyub95BP47DO44AIYMuT0vRFlv2PcP3sa3rDBuqdiwAAYPPj0dHc1DW/aBBkZ0K+fdUVWXWzaZHUF\n0q9f7c7BVI7x5ZfW/SJ1ifH55/Vb3m4x3ngDZs7075Vw7dq14+DBg2RlZfkvaBMTHR1Nu3btAreC\nmn4aAGOAF92GJwBPV5rnaWAD0AxIBr4DzvEQawqQAWSkpKT47/dRA/NHe6i/flZXPndRl5/l9Y1h\nhxzsEsMOOdgphvI/GrDNPw34yG14BjCj0jzTgfvdhl8CxlYXN9gnfIPNLjsQbef2Xww75GCnGCow\nGrL4hwO7gU6cPuHbo9I85wIrXfPGAluAntXFDfXi7w92uKrDDjnYJYYdcrBTDBUY/ir+Pt3ha4y5\nHHgC68qfhSIyxxgz1dVsNN81z13AzYDT1UxUbVd9wTzhq5RSjZW/Tvg26e4dlFKqqdHuHZRSStWZ\nFn+llApBQSv+mZmZpKenk2+Hu5yUUirEBK3N3xgjMTExxMbG8u677zJ06NCg5KGUUo1Jk2jzP3Xq\nFNnZ2bzwwgvBTEMppUKOtvkrpVQIskXxLygoCHYKSikVUoJa/CNd3QyuWbOGTZs2BTMVpZQKKUEr\n/g6Hg7vuuovPPvuMZs2aMWTIEBYsWECwTkArpVQoscUdvj/99BPjxo3jo48+YtKkSTzzzDNER0cH\nJS+llLKzJnG1T5mWLVvyz3/+k7/85S8sXLiQIUOGsG/fvmCnpZRSTZYtij9YzUAPPvggy5Yt49tv\nv6Vfv3785z//CXZaSinVJNmm+JcZPXo0GRkZtGnThssuu4y5c+fqeQCllPIz2xV/gK5du7Jhwwau\nu+46ZsyYwbXXXktubm6w01JKqSYjeMX/yBFYv97r5GbNmrF48WIee+wxli9fzsCBA9m+fXsDJqiU\nUk1X8Ir/oUNwySXV7gCMMdxxxx2sXLmSY8eOMXDgQJYuXdqASSqlVNMUtOK/IwmOhBfA6tU1zjts\n2DC+/PJLevbsydixY5k2bRrTp0+nRYsW2jOoUkrVQfB69Wxr5Dfnw7PTPoaf/cynZQoLC7n++utZ\ntmwZYWFhOJ1OtGdQpVQoaRLX+T83AMxnPyd6tm83dEVFRREfHw+A0+kETvcMumDBgoDlqZRSTU1Q\ni79x/ehwSBi/Xv5r1h1YV+fLOt977z1mzpzJrl27/JihUko1TUEr/sYYjAnj6j3R3LArije3vMmF\nCy/k3GfO5ZHPHuHwicO1ipeQkMCcOXPo0qULI0aMYNGiRXouQCmlvAha8T83+Vym9p+K6duXl145\nzpGSaSwcvZBWzVpx93/upv3j7Rn9xmje/+Z9ikuLy5e75ZZbSEpKIiYmBoCYmBiSkpJ488032bdv\nH7Nnz2b//v1MnDiRM888k1tvvZWNGzfqjWJKKeVORILy6tevn5S7/nqRyEiR7dtFRGTHjztk+sfT\npc28NsIspNUjreSPH/1RthzdIiIieXl58od7/iCOXzvk9vTbJS8vT9yVlpbK6tWrZeLEiRITEyOA\nnHfeeTJv3jw5cuSIiIicPHlSZsyYIYmJiXLPPfdUiaGUUnYEZIgfarAtevXk6FE491zo2dO69DPM\n+kFS4izho50fsTBzIct3LKfEWcKgswYxqe8kNh7cyMtfv8yt/W7l2Sue9bqe3Nxc3nrrLRYuXMiG\nDRsIDw9n8ODBfP3115SWlpKfn69XDCmlGg1/Xe1jj+IPsHAhTJ4MCxbALbdUmT8rL4vXNr/GH//9\nR4SqOUc6Itl7+17OjDsTY4zHdW7fvp1//OMfPPnkkxQWFlaZPnLkSN544w0SExPr/ocppVQANb3i\nL2Ld8fvll7B9O7Rp43G573O/Z9LySazcvZISKakyvXlUc7oldaN7cne6J3cv/9ylZReiwqMAGD9+\nPIsXL4Y4YAywFDh5OsaZZ55Jt27d6N69e/mrW7dupKSk4HA4AMjLy2POnDk899xz/Pa3vyU9PZ3Y\n2Fg/bR2llPKsSVznX4Ex8PzzUFAAf/iD19naNm9Lp8ROOHESHR5NmAljQuoEPp7wMU+PepqJqRNJ\niE7gk72fkL4qnTFLxtDzuZ7EPhRLlye7cOXiK/mq1VdwPnAF0AG4BIiAocOG8vDDD3P55ZdTXFzM\n22+/zR133MGoUaPo3LkzcXFx9O7dmxEjRtCqVSvmzZ/H8auP87fn/0a7du1YuXJlrf7kvLw8/nDP\nHwi/JZzb02+v09VJdohhhxzsEsMOOdgphrIv+xz5l3noIUhPh2XLYPRoj8te89Y1tIlrw5R+U1jw\nxQIOnzzMu9e/W2W+k0Un+Tb7W7758Ru++fEbdmTv4Jsfv2Hz0c1e80qKSaJ5VHMSohNIiEogxsQg\nBULRiSLyf8on54cc9mzfQ2FuIfQCzgG2Af8FSiEyPJL42HiaN2tO87jmJMYlkhCfQIvmLUhMSCSh\neQIJCQkcPXqUZ599lvwR+Tj7Ogn7Koz4tfE89dRTpKWlERkZSURERPl72eeyXx4Aa9eu5ZprruH4\nRccp7V2KI9NB4meJtTp3Ud8YdsjBLjHskIOdYqjAaHrNPmWKi6FfP/jpJ9i2DZo39/u6D+Ue4tbl\nt/Kvnf+i1JQSJmH0bN2TYR2HUeosJacwx3oVnH7PLcwlpzAHpzjrt/JS1ysC8HRqQoBs17uXl3H9\n52zr9BzDCbFZsRjXxPJ3Y6p8Pp503PPvPyckHUuy5jeudZvTscri/pD4g9flW+e0rjDKfVn3j0ea\nH/Eao02u5+a/yg43P+w1Rtvctj7F+L75995jnKg5xvfx9Vu+McRwOB2U3F+1uVU1HH8V/3B/JONX\nERHwwguQlgb33ANPP+33VZzV/CzaJ7ZHwoRoRzRFpUVcmHIhT456strlRIS84jyu+811rDi1As7G\n2oIlwEEYGDuQ39/6e4pLiykqLaLYWVzlc0FJAfkF+Sz/eDn72Q+JgANwAjmQVJxEl05dKHWWVng5\nxVnlc1ZWFqdiT0Es1j9UJ5APkfmRxMbHWpd0uU6Ol+3kxfUfAk6chP8UTklsCUS7xSgAR76DgsiC\nKn9/edF2HTOEHQvDGeu0li/bSRRAWF4YuRGnn8Hg6SR9GXPMILFSJYbJMxyLOFbt/5MaY+QbssOz\nGySGHXIIWIwi4Bu4Ov5qn5ZX9me/4g8waBD8/vfw1FMwfry1I/Czo3lHmdpvaoWmo5oYY4iLjGP6\nTdNZ9eRwCAJGAAAOz0lEQVQqCsMKoRhwQFRuFI/e/ChDU337SXz87eO8duw16Ed5DHbCqBajWPSn\nRT7FmDBhAq/tqxRjO1zX4joWPVOLGHsqxdgKN7a4kUWLao4xYcIEXtvtWr7EtfwWGNdinE/LVxdj\nfIvxjSqGHXIIWIxwoBBiYmN8Wl41Av64WaAurwo3eXmSmyvSvr1Ijx4ihYU+3wDRUEa/PlrOn3m+\nxHeJl34z+8no10fXavk1a9ZIxP9FiGO0Q2iNOEY7JOL/ImTNmjWNKoYdcrBLDDvkYKcYKjDw001e\n9i3+IiIffGCl+OCDddhE9peXlyf33HOPJCYmSnp6ep3uMrZDDDvkYJcYdsjBTjGU//mr+NvvhG9l\nN9wA770HmzdDt26BT0wppWysQa/zN8aMNMbsMMbsNMZMr2a+AcaYEmPMmPomVu7vf4fYWJgyBZz1\nvNJGKaUU4EPxN8Y4gGeAUcB5wI3GmPO8zPcw8G+/Zti6NcybB2vXwksv+TW0UkqFKl+O/AcCO0Vk\nt4gUAW8CV3mY7/fAO8APfszPMmkSDB8Od90Fh2vXz79SSqmqfCn+ZwEH3IYPusaVM8acBfwSeM5/\nqVVYgdXhWw1dPyillPKNv/r2eQK4W6T621+NMVOMMRnGmIysrKzaraFrV7j3Xli6FJYvr0eqSiml\nfCn+h4D2bsPtXOPc9QfeNMbsxeon81ljTJVbAUVkgYj0F5H+rVq1qn22d90FvXrBb38Lubk1z6+U\nUsojX+7w/RzoaozphFX0bwDGuc8gIp3KPhtjXgY+EJH3/Zinxb3rh0mTrD6Ahg8PyB3ASinVlNVY\n/EWkxBhzG/AR1o3iC0VkqzFmqmv6/ADnWNGgQTBmDCxZYl3/HxUFK1fqDkAppWrBp759RORD4MNK\n4zwWfRH5Vf3TqsF5ritNnU4oLLQe/ajFXymlfGafh7nUxmWXQXS09dnphB07oLQ0uDkppVQjYs9e\nPWuSlgarVsHHH8Pnn8Mrr8CBA7B4sXVTmFJKqWo1zuIP1g6grKnnH/+wrgDq2xfeeguGDAlubkop\nZXONs9mnsptvho0boVkzGDECHn3UeiC8Ukopj5pG8QdITYWMDLj6avjzn+GXv4Tjx4OdlVJK2VLT\nKf4ACQnWJaCPPw7//Kd1H8CXXwY7K6WUsp2mVfzB6gdo2jRYs8a6DPSCC6wbw7QZSCmlyjW94l/m\nggvgq69g6FDrWQC/+hXk5QU7K6WUsoWmW/wBWrWCFSvgvvtg0SIYPNi6J0AppUJc0y7+AA4HzJoF\n//qX9SyA/v3h7beDnZVSSgVV0y/+ZS691GoG6tkTrr8ebr/dejrYX/8K69cHOzullGpQjfcmr7po\n3946EXz33fDEE/D009Z47RxOKRViQufIv0xkpHUp6LhxVr9ATiecOmXdIfzuu3pSWCkVEkKv+Je5\n7TaIiYGwMOu8wK5dcO21kJQEv/gFvPgiHD0a7CyVUiogQqvZx11amtXUs3q19UCY/v3h009h2TJ4\n/3344APrnoG0NOuu4auugnPOCXbWSinlF0aCdPNT//79JSMjIyjrrpEIbN58ekfw1VfW+O7dT+8I\nBg60+hMq23no+QKlVAMwxnwhIv3rHUeLvw/27bMeGr9smXXCuKQEWraEnBxrRxEZaXUncfHFwc5U\nKdXEafEPlmPH4MMPYe5c2LKl4rSWLSElxXq1b1/1c5s2EF6ppW39ev31oJTymb+Kf+i2+ddVixYw\nfjx07gyXXGL1HxQebj1Q3hjYvx/27rXuIajcq6jDAW3bnt4phIVZN5yVlloPp3/8cesZxbGxVvfU\nZa/ISCu2N/7YgdQ3hu7ElGpUtPjXVeUTxp4KXm6u9YSxAwesnULZ68AB63zB3r3WpaZg7UR++1vP\n63I4rJ1A5Z1Cs2bWcuvWWXHCwqwrldq1s3ZI3l4OR8XhffvgsceguNjaCU2fbp3cDguzdjpl794+\n79gB6elWc1hEhHXjXNlzlst2WjW9A2zbBl9/DX36QI8eFbeBt51f5fFbtpyO0bOn52VqsmULZGZa\nDweqS4wtW6zzROefX78cvvqq7jn4M8bBg3D55bpTb2K02SeYPvsMfvYzq+iGh8PDD0OHDpCfb91v\nUPaqPOw+bs8eOHLkdMyyXwqlpVYxLnuV7WSUqq2wML0R0ka02acpuPBC61nE9W1uueQSKCqyiv7H\nH3uO43RW3SGUDW/cCDfccPrI/5VXrCNnEWs593dPn7/+Gn73u9PLP/kk9Op1uhvtmt4BXnsNXnrp\n9C+YSZOs5rXK87mrPH7xYuuRnmUxfvUr62a+2li8GF5+uWKMG2/0ffk33qi4/E031W75shivvGKv\nGEVF1vdUi3/TISJBefXr10+Un6xbJ/LQQ9Z7sGL4Y/mYGBGHw3qvSxw7xLBDDnaKofwOyBA/1GBt\n9lH2YYcT1/6IYYcc7BRD+ZVe6qmUUiHIX8U/dPv2UUqpEKbFXymlQpAWf6WUCkFa/JVSKgRp8VdK\nqRCkxV8ppUKQFn+llApBWvyVUioEafFXSqkQ5FPxN8aMNMbsMMbsNMZM9zB9vDFmszHmf8aYdcaY\n3v5PVSmllL/UWPyNMQ7gGWAUcB5wozHmvEqz7QGGiUgv4EFggb8TVUop5T++HPkPBHaKyG4RKQLe\nBK5yn0FE1onIMdfgBqCdf9NUSinlT74U/7OAA27DB13jvJkMrKhPUkoppQLLrw9zMcaMwCr+F3mZ\nPgWYApCSkuLPVSullKoFX478DwHt3YbbucZVYIxJBV4ErhKRbE+BRGSBiPQXkf6tWrWqS75KKaX8\nwJfi/znQ1RjTyRgTCdwALHefwRiTArwLTBCRb/2fplJKKX+qsdlHREqMMbcBHwEOYKGIbDXGTHVN\nnw/cCyQBzxpjAEr88bABpZRSgaFP8lJKqUZEn+SllFKqzrT4K6VUCNLir5RSIUiLv1JKhSAt/kop\nFYK0+CulVAjS4q+UUiFIi79SSoUgLf5KKRWCtPgrpVQI0uKvlFIhSIu/UkqFIC3+SikVgrT4K6VU\nCNLir5RSIUiLv1JKhSAt/kopFYK0+CulVAjS4q+UUiFIi79SSoUgLf5KKRWCtPgrpVQI0uKvlFIh\nSIu/UkqFIC3+SikVgrT4K6VUCNLir5RSIUiLv1JKhSAt/kopFYK0+CulVAjS4q+UUiFIi79SSoUg\nLf5KKRWCtPgrpVQI8qn4G2NGGmN2GGN2GmOme5hujDFPuqZvNsac7/9UlVJK+UuNxd8Y4wCeAUYB\n5wE3GmPOqzTbKKCr6zUFeM7PeSqllPIjX478BwI7RWS3iBQBbwJXVZrnKuBVsWwAEo0xbfycq1JK\nKT/xpfifBRxwGz7oGlfbeZRSStlEeEOuzBgzBatZCKDQGLOlIddfR8nAj8FOwgeap381hjwbQ46g\nefpbN38E8aX4HwLauw23c42r7TyIyAJgAYAxJkNE+tcq2yDQPP1L8/SfxpAjaJ7+ZozJ8EccX5p9\nPge6GmM6GWMigRuA5ZXmWQ5MdF31MxjIEZHD/khQKaWU/9V45C8iJcaY24CPAAewUES2GmOmuqbP\nBz4ELgd2AvnAzYFLWSmlVH351OYvIh9iFXj3cfPdPgvwu1que0Et5w8WzdO/NE//aQw5gubpb37J\n01h1WymlVCjR7h2UUioEBbz4N4auIYwx7Y0xnxhjthljthpjbvcwz3BjTI4xJtP1ureh83TlsdcY\n8z9XDlXO+ttke3Zz206ZxphcY8y0SvMEZXsaYxYaY35wv8zYGNPSGPOxMeY713sLL8tW+10OcI6P\nGmO+cf0/fc8Yk+hl2Wq/Hw2Q5yxjzCG3/6+Xe1m2QbZlNXm+5ZbjXmNMppdlG3J7eqxDAft+ikjA\nXlgniHcBnYFI4GvgvErzXA6sAAwwGNgYyJy85NkGON/1OR741kOew4EPGjo3D7nuBZKrmR707enh\nO3AE6GCH7QkMBc4HtriNewSY7vo8HXjYy99R7Xc5wDleCoS7Pj/sKUdfvh8NkOcs4E8+fCcaZFt6\ny7PS9L8B99pge3qsQ4H6fgb6yL9RdA0hIodF5EvX5xPAdhrvHcpB356VXALsEpF9QcyhnIisBX6q\nNPoq4BXX51eAqz0s6st3OWA5isi/RaTENbgB616aoPKyLX3RYNsSqs/TGGOA64A3ArV+X1VThwLy\n/Qx08W90XUMYYzoCfYGNHiZf4PrZvcIY06NBEztNgP8YY74w1h3Tldlqe2LdF+LtH5YdtidAazl9\nX8oRoLWHeey0XSdh/brzpKbvR0P4vev/60IvTRR22pZDgKMi8p2X6UHZnpXqUEC+n3rC140xJg54\nB5gmIrmVJn8JpIhIKvAU8H5D5+dykYj0wepJ9XfGmKFByqNGxropcDSwxMNku2zPCsT6DW3bS+CM\nMelACfC6l1mC/f14DqvpoQ9wGKtJxc5upPqj/gbfntXVIX9+PwNd/P3WNUSgGWMisDb46yLybuXp\nIpIrIiddnz8EIowxyQ2cJiJyyPX+A/Ae1s89d7bYni6jgC9F5GjlCXbZni5Hy5rGXO8/eJgn6NvV\nGPMr4EpgvKsIVOHD9yOgROSoiJSKiBN4wcv6g74tAYwx4cA1wFve5mno7emlDgXk+xno4t8ouoZw\ntfu9BGwXkce8zHOmaz6MMQOxtl12w2UJxphmxpj4ss9YJwErd44X9O3pxutRlR22p5vlwE2uzzcB\nyzzM48t3OWCMMSOBPwOjRSTfyzy+fD8CqtL5pV96WX9Qt6WbnwHfiMhBTxMbentWU4cC8/1sgDPY\nl2Odtd4FpLvGTQWmuj4brIfF7AL+B/QPdE4ecrwI66fUZiDT9bq8Up63AVuxzqJvAC4IQp6dXev/\n2pWLLbenK49mWMU8wW1c0Lcn1s7oMFCM1S46GUgCVgLfAf8BWrrmbQt8WN13uQFz3InVplv2/Zxf\nOUdv348GznOR63u3Gav4tAnmtvSWp2v8y2XfR7d5g7k9vdWhgHw/9Q5fpZQKQXrCVymlQpAWf6WU\nCkFa/JVSKgRp8VdKqRCkxV8ppUKQFn+llApBWvyVUioEafFXSqkQ9P8BedL5CZ5oLYoAAAAASUVO\nRK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1196aa908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "[prev, pred, upper, C1, C0]=process(res0, l0, trans, guess, slip)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity stop at here 4 th problem\n",
      "stability stop at here 6 th problem 0.000766240605864\n",
      "++ condition is met\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nhAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1186d13c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "[prev, pred, upper, C1, C0]=process(res1, l0, trans, guess, slip)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "similarity stop at here 12 th problem\n",
      "stability stop at here 14 th problem 0.0009234387817\n",
      "++ condition is met\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXdYVMfXx7+zS1uKUkVEsXdFCPYWjYnRaPRnLIm91xhb\nEl9LVKJgLFijsUXUWGNLNNaoFFFQY0FFEAtWUECw0Mvuef8YabJsYZcizud57rPsvVPOXuB7Z8+c\nOcOICAKBQCD4sJCUtAECgUAgKH6E+AsEAsEHiBB/gUAg+AAR4i8QCAQfIEL8BQKB4ANEiL9AIBB8\ngAjxFwgEgg8QIf4CgUDwAaJW/Blj3oyxGMZYSAHXGWNsNWPsHmPsBmPsI/2bKRAIBAJ9YqBBma0A\n1gD4o4DrXQHUfnu0ALDu7atKbG1tqVq1ahoZKRAIBALOlStXXhCRna7tqBV/IjrLGKumokhPAH8Q\nzxNxgTFmyRhzIKJnqtqtVq0aLl++rJWxAoFAC4KCAD8/oEMHoFWrkrZGoCcYY4/00Y4mI391OAJ4\nkuv907fnVIq/QCBQQZZwt28PODsDr18Db97kPwo6HxkJhIQARIBEAnz0EeDoCJiZAaam/FXZ8e61\n8HAgNBTo3Fk8QMoY+hB/jWGMjQEwBgCcnJyKs2uBoPRCBERFATdu8OPMGeD0aX5eU8zMgHLlgPLl\n+Wtyck59hQKIiQHS04GkpLyHQqG+bYkEWLyY2yUeAGUGfYh/JIAqud5XfnsuH0S0EcBGAGjatKlI\nJyr48EhJ4SPp69dzxP7GDSAuLqdM+fI5ws0Y0KUL0KtXjrC/e1hYAFJp3n6CgoBOnbjgGxkBe/bk\nF24i5Q+ErGPHDl5PoeDl/PyE+Jch9CH+hwFMZIztAZ/ofa3O3y8QlGmCggBfX6B+fcDQkIt7ltjf\nuZMz2jY1BRo3Br76irt2mjTh78PC8gr3nDnai26rVnykrsrnzxhgbMwPa+v81y0tgb/+yrGjQwct\nb0TBZGRk4OnTp0hNTdVbm2UNExMTVK5cGYaGhkXSPlOXz58xthtABwC2AKIBzANgCABEtJ4xxsCj\ngboASAYwnIjUzuQ2bdqUxISvoMxx+jTQtSuQmZn3fPXqOQLv7MyPGjXyj9izKC2TtUVkx4MHD2Bh\nYQEbGxtwCRHkhogQFxeHhIQEVK9ePc81xtgVImqqax+aRPv0V3OdAHyrqyECwXuNXA5s3QpMmZIj\n/BIJMHo0sGQJd89oQ6tWpcPFUkR2pKamolq1akL4C4AxBhsbG8TGxhZZH2KFr0CgK/7+QNOmwKhR\nfIRvbMxH9MbGwNCh2gv/B4IQftUU9f0R4i8QFJaICKB3b+4SiY/nk6PXr3N//4IFIjpGUKop1lBP\ngaBM8OYN4OkJrFzJJ3Q9PIBp0wCZjF8vLS4bgUAFYuQvEGiKXA5s2gTUrs39+P378+id2bNzhF/w\n3iCVSuHi4oJGjRqhb9++SE5OBgCYm5sXWOfhw4fYtWtXcZlYpAjxFwg0wdcXcHMDxozh4v/ff3yC\nt1KlkrbswyEoCPjlF/6qB2QyGYKDgxESEgIjIyOsX79ebZ2yJP7C7SMQqOL+feDHH3m8e9WqwJ9/\nAn378hh5gX6YMgUIDlZd5vVrvk5CoeBRVM7OfNFbQbi4cLechrRr1w43btxQW27GjBkICwuDi4sL\nhg4diqlTp2rcR2lDiL9A8C5BQcCJE8C9e8D+/dyv7+kJTJ0q3DslxevXOYvjFAr+XpX4a0FmZiaO\nHz+OLl26qC27aNEieHl54ciRI3rpuyQR4i8Q5CYwkEfvZGTw9926cT+/g0OJmlWm0WSE/m66ip07\ndZ5UT0lJgYuLCwA+8h85cqRO7b1vCPEXCHLz6685wi+VAm3aCOEvDWiSrkJLsnz+HypC/AWCLF6/\nBv79l/vzJRK957MR6EgpCKG1sLBAQkJCidqgL0S0j6D0oOdoDq2ZNQt49Qrw9haLtARKcXZ2hlQq\nRZMmTbBixYqSNkcnxMhfUDoICgI6duQ+XROT4hfeS5eAdeuASZOAYcOKr19BiZGYmKjVeQAwNDSE\nj49PUZlUrIiRv6B08O+/QFoazzGflsZ9u8VFZiYwdiyP2V+woPj6FQhKEDHyF5QOYmJyflYogDp1\niq/v1at5nPmBA3xjFMEHz82bNzF48OA854yNjXHx4sUSskj/CPEXlDxv3vCkaC1bAu3aAb/9Bnh5\nAT17AgZF/Cf6+DEwdy7QvTvfLUsgANC4ceMyHwkk3D4CTklOtq5cybNirlnDc+b8/jtw4QIwf37R\n9z1pEnc1rVkjVu0KPijEyF/A89F/+il3txgbF+9ka3w8sGwZH3W7ufFz33wDnDzJV9V++inQvn3R\n9H3oED+WLOGpGwSCDwgx8hfwUW9mZt6NuosLLy8gIQH4+ee851ev5tscDhoEvHyp/34TE4HvvuN7\n5k6Zov/2BYJSjhD/Dx0i4ObNnPcSSfEtbIqJAVat4iP9xo3zXrOwAHbtAp4945k01ew1rTXz5gFP\nnwIbNvDcPQLBB4YQ/w+dCxeA8HC+GUm1ajzGvnbt4ul70SIgNRVwd1d+vVkzvlHK/v3Ali366zc4\nmD90xowRi7jeE5KSkjBr1ixYWVlh9uzZ2bn3daGgfP6a8vfffyM0NFRnO0oMIiqRw83NjQSlgG++\nISpXjighgSgkhMjAgGjkyKLv9+lTImNjouHDVZeTy4k++YTI1JTo9m3d+83MJGrenKhCBaL4eN3b\nExSK0NBQjcv6+/uTjY0NmZqaEgCSyWRkY2ND/v7+OtlgZmaW/fOAAQNo2bJlWtUfOnQo7du3Tycb\n1KHsPgG4THrQYDHh+yETFcVH1RMnAubmQMOG/BvAkiXAiBFA69ZF17enJ59jmDtXdTmJBPjjD56/\nfcAAHo1kZFT4fjds4Kt5d+4ErKwK345Ab0yZMkVlWGVYWBji4uKy36ekpCAlJQV9+/ZF/fr1ldZx\ncXHBykLm8//jjz/g5eUFxhicnZ2xffv2fOUDAwNx+PBh+Pv7w8PDAwcOHEDNmjU17q80INw+JU1J\nhlhu2MC3Jvz225xzc+YAVaoA48fzSeCi4MEDHs45ahR3NanD0RHYvBm4ehX46afC9/vsGTBzJo8g\n6t+/8O0IyhRZ+fwbN26MW7duwcPDAz4+Prh+/TpWrVqltE7r1q3Ro0cPLF26FMHBwe+d8AMQbp8S\nJTCQuz4YI5LJ+PviIjWVuz66dct/7cABIoBoxYqi6Xv4cP65nz7Vrt64cdyuU6cK1+/XX/N+794t\nXH2B3tDG7TNo0CACkO8YNGiQTjZIJBJq0qQJNWnShCZOnEhpaWm0evVqmjVrlkb133e3jxj5lyR+\nfjn5bIo7xHLfPh5tM2lS/mu9egFdu/JvAZGR+u33zh1g2zZgwgQ+oteGZcuA+vWBIUOA2Fjt6p44\nwbdg/OknoFYt7eoKSpTRo0fDxsYGsre7qMlkMtjY2GD06NE6tZuVzz84OBi//vorjHRxJ76HCPEv\nSXJH1RAVb+741auBunW5C+RdGMvZ1OT77/Xbr7s7jyiaMUP7uqamwO7dQFwcMHKk5uGfycn8YVOv\nHt+PV/Be0b59ezx+/BhTp06FpaUlpk2bhsePH6N9ESz+++STT7Bv377sOYb4+PgCy773uf318fWh\nMIdw+xDRggXcjdGqFX/18yuefi9c4P2tWaO63M8/83L//quffm/c4C6umTN1a2flSs3sz2LmzOK9\nvwK1aOP2KSpyR/vkZuvWrdSwYUNydnamoUOHFlj/3LlzVL9+fXJxcaF79+4ViY1F6fYR4l9SKBRE\n9eoRtW9PlJxMVKkSUZs2/HxRM2AAkYUF0Zs3qsulpBDVqkVUuzafI9CVXr14WGlcnG7tKBREXbty\n//3Nm6rLZoWvDhumW58CvVIaxP99QPj8yyLXrgG3bwMDBwIyGfevnz/PfdNFybNn3N8/YoT69MUm\nJsDatcDdu8DSpbr1e+UK8NdfPJTU2lq3thjji77Kl+dROykpysspFMC4cbycrvYLBGUMIf4lxc6d\nPK1Anz78/YgRQPXqwOzZXLSKig0buC8/d3inKjp3Bvr25XH5Dx4Uvt85c7jo6yuPjr09nzgOCQGm\nT1deZssW4Nw5Lvy2tvrpV/DB4enpCRcXlzyHp6dnSZulO/r4+lCY44N2+2RmEjk4EPXsmff8H39w\nT9zevUXTb1oakb090RdfaFfv6VMic3MeFloYt9S5c/xzLVqkfV11TJnC2/7nn7znY2KIrK25W604\nXGkCrRBuH80Qbp+yhp8fd78MHJj3/IABQIMGfNVrUSyw2rcPiI7m2Sy1wdGRZ908ehQ4fFj7fufM\nASpU4CuJ9c2iRUCTJsDw4fyeZvHDDzxb6Pr1Ik+/QKAEIf4lwc6d3N/evXve81Ip30P29m1gxw79\n9/vrrzy8tHNn7etmpT+eNAlIStK8no8P4OsLzJoFmJlp3686jI159s+kJGDoUO4y8/XlKSGmT+fr\nAgQCQT6E+Bc3qal8r9jevflE77tkbWri7s4XgOmLS5eAixe5iEsK8Ws3NOTbKz5+zDNtagIRX1Tl\n6Mg3SC8qGjQAVqwATp3i8fz9+vE+Z88uuj4FAj2xcOHCEulXIxVgjHVhjIUzxu4xxvKtzmGMlWeM\n/cMYu84Yu8UYG65/U8sIR4/yPWvfdflkwRifXH30iOe/0Re//sqTtw0dWvg22rYFhg3jG7Boksr2\n+HGes2jOHB45VJSMGcN3/NqwAXjxgh9lfA9WwfsDEUFRQCBHqRV/xpgUwFoAXQE0ANCfMdbgnWLf\nAggloiYAOgBYxhgr02ulo3dGI6haEPwkfgiqFoTondGaVdy5E6hYEejYseA2OnfmQubhwVen6mrH\n8+c8tcHw4UC5crp9liVLuMvq22+zV9gqrZ816q9enferhkLfzywYQ7R1XwRhN/xwBkFp2xC94qb6\nenq0Y8kS4MDs+Dz1D8yOx5Ilmve/ZAmwe/pzHC9/HD7MB8fLH8ee6dGFamNnpZ1oMrwJdjrsLHQb\nutihT3T++1DCw4cP0ahRo+z3Xl5ecHd3R4cOHTB58uTsXP+XLl0CALi7u2Pw4MFo1aoVateujU2b\nNmXXXbp0KZo1awZnZ2fMmzcvu/26detiyJAhaNSoEZ48eZLPhhkzZiAlJQUuLi4YWNCAsIjQJKVz\ncwD3iCgCABhjewD0BJB76EcALBhjDIA5gHgARZQSsuSJ3hmN8DHhUCTzJ3naozSEjwkHANgPtC+4\n4suXfOQ/YQKi97xQ2QZ5eADtO4BWrQVNnQYoAJJT9ispCLF7Y3H/h/tQpORqY3Q40mPTYfs/25wU\nWKv/AGXYAV+OA+4m55wHEHs4Fo/mPoIiNVcbo8KRGpkK2+622eVyMAVNXAYsmA8s3os41hKP3JXU\nP34FNtdeAQs8gPB0AOlK2uLEHY3Do/lK2nicCptuNhr9TuKOxuHRsUbIGlulwR7hh+yR+ssj7drQ\nwY4qdxIxcrM15kEGV6ThwiMZfl5ojnUjnyPxhrlGNhievYfxRxtgqrkBDg6biq/2L8OKpVaY0zUY\nLzvVgoL4tIZcwV8VCmSfIwWDgoCkfx9g7Jk6qNPtH9x0uokVbv/gztK+mNYpBPebVoeEAWAEiYR7\nABmQ72f43ce44/UxxdwQf7+1Y/lSK7gPCgbgotFn0ReF/n/TgeTkZAQHB+Ps2bMYMWIEQkJCAAA3\nbtzAhQsXkJSUBFdXV3Tr1g0hISG4e/cuLl26BCJCjx49cPbsWTg5OeHu3bvYtm0bWrZsqbSfRYsW\nYc2aNSpTWhcVjEh1fhTGWB8AXYho1Nv3gwG0IKKJucpYADgMoB4ACwBfE9FRVe02bdqULl++rKP5\nhSd6ZzQiZkcg7XEajJ2MUcOzhkZ/SKQgBFUNQvrT9HzXpOWlqDSuEhQpCihSFTmvWT8/jIL83mMo\natRH8mNFwY9HhgKFsiyyG1VQDwlwxavsc9dgiduwQH/kHy2VJjvkAF7DEC9hhJcwwjVY4iAqoxYS\ncAcWaIp4lEcmMiBBBhgyIEE6JCrfp0KK1G4TAbeNwJWxwNHflBvM5IDsJSCLA0xfAKZx/OcvxwBS\nJX9ccgPg0BYgtTyQVv6d13IASfPX6TYBcFsPoyujsejoeNQye4IvE78sxN3NS1hYWHYu/rtT7iIx\nOLHAsm8uvAGl5f+HYMYM5Vrm/yYLAOYu5qi9UvWOdA8fPkT37t2zhd3LywuJiYnw8/PD3Llz8ckn\nnwAAnJyccOPGDaxcuRIKhQLz588HAAwZMgRfffUVzp07h/3798PS0hIAkJiYiJkzZ6JTp07o2LEj\nHqhZH2Nubo7EROWfP/d9yv7cjF0hoqYqG9UAfW3m8jmAYACfAKgJ4BRjLICI3uQuxBgbA2AMwG9o\nSVHQSEKRoYBle0ukRabx42ka0iPTs39Oi0xD+rN0UIZyZZa/luPpyqeQmEgglUkhMZFAIpPwVxMJ\nJC+ewcgsHRJXayRHvCjQvqqzqwJSgD1/DrZhLfBJR7DPPwMkAJMyMAkDpMC97+4V2EbdLXUBAOzi\nRWD9OuD7aYBLE36OMf6AYcDcAYkFCt6CP9+uAFYWKRkRAcyciVCaW0ABQsOpr4E2bfKeVlK0Xu9I\n/IwGmIdQuOIVrsEy+33DAw0L/Iy5udXnlvIHJgMa7tesjSw75iAM1ZGEQFhjPWqhByLhO7gpYl9L\nEPtKgtjXEsS85K/xCQwKRf4PFQJLGECBEFjCEApILDIgYRmQUDpAyYAiDYrMFH4oUt7KfzrezO4N\nhWGuif5m64Bm68AUEphHNIHcNBFyWQIyZYmQGycBrIBRgoLxa1kDCQb+QPhqcIGfX5JuAmm6KaRp\npki1jszTdnqzjZjWbCOMMoyQBj0GImiAMuFXdV5TDAwM8vjhU1NTs39m74QHZ71Xdp6IMHPmTIx9\nJ6jh4cOHMCuKCDc9oYn4RwKokut95bfncjMcwKK3CxDuMcYegH8LuJS7EBFtBLAR4CP/whqtKxGz\nIrKFPwtFsgLhw8PzlZWYSWBc2RjGjsaw/NgSxpWNEbU+Cpkv84+sjJ2M0epRAXvCPn0KOI3iUTxz\nG+Fbq0eo9eplPtG9Z2mFtQuqvj1THYhbDpyYAuyJAOzs8jT5xOsJ0h7l/0c0rmoMh2EO/M3GJUCt\nWGDJZ0qjfBpPfYGfovMLr4f9XVTop2qDigrA00a4vyYaaaiY3wbDl7Dz+p9GkUUtq97D3EehmIuG\n+AgvEQwruOMWWlZNgd1XdmrrA/zeK70XTsZq2yDimaYfWiXB/mUqfoAzcj+ldqMqsJ3PWVesyBcX\n13YB2trzn+3tAVtbOSKGBiEh3Qa/oRZ6IhKHUQnzEAoH3Eb/BL55TLly5VDRoSLs7e2zD4sKFkiw\nTECUYRRuBFfG/YoRgCTn34PJJbBKtkTVtoCNaVXYyGxga2oLG5kNbEzz/3yx+kWsarMfQW4HIc00\nhFyagZZX/ofxF3uiZVhLvE59jddprwt+TXuN26du46H1UyTLEvhDIF2Gj253xORzXwMaBntpiroR\nelC1oAL/zl39XAvdr729PWJiYhAXFwdzc3McOXIEXbp0AQD8+eef6NixI86dO4fy5cujfPnyAIBD\nhw5h5syZSEpKgp+fHxYtWgSZTIY5c+Zg4MCBMDc3R2RkJAwNDTW2w9DQEBkZGVrV0QeaiP9/AGoz\nxqqDi/43AAa8U+YxgE4AAhhj9gDqAojQp6G6oshU4PXZ14jZG4O0xwWPXOp6180We2NHY0jLSfM9\n7c0amuX55gAAElMJaiysUbABu3dzlRnAb90nEywwcqFDPtHdPOGdr3/z5wMHDwKLF/Mom1zU8Kyh\n3A7Pt3ZcvsyjbVauLFCE+y6zRczw+5iV0RjNEYdgWOFn41D0XZZf0POxYAFq/DES4YljoVDkzO9L\nkIoaE000Ev7bt4F9Lo2x57EUiWSIs6gAAFjFauN6LYYne4GPP+YCqwq19+IdkpL4coDjx/nBv5lX\nQ1WWhPr0BmEoj/aIQV/jKLh5VkbD0bawsMi7Xiw6OhonTpzA8ePH8e+//6Ja+nDcxy+YZu6Hg31+\nwNT9y+Ce2B6dawTioc9D2Nvbw8TEBE/fPEXAowAEPA6A/2N/hMSEAC8BQ4khHK3qQxLdGIqKN2Eo\nN0CmJBMGV0fhJ6vxmOqlma/9r+6v8F96Btpc7oNhVz7DVrdTuGiehrjPnVHHpo5Gbaz4Jxj/d+s3\nMLffYZhphAyDVNxMq4y4zs4a1dcn2v5uNcXQ0BBz585F8+bN4ejoiHr16mVfMzExgaurKzIyMuDt\n7Z193tnZGR07dsSLFy8wZ84cVKpUCZUqVUJYWBhateIDP3Nzc+zYsQNSqRI3mhLGjBkDZ2dnfPTR\nR9i5c6dOn0kb1Pr8AYAx9gWAlQCkALyJyJMxNg4AiGg9Y6wSgK0AHMCHTIuISOUqpeLw+WcJfuy+\nWMQeiEVGbAYkZhKAkG/kD/CRRKuHBYzc30HrOQMXFz50vHAh+9SB2fEYscgCbRWxCJTY4fcZCejt\nqSTp2bBhwJ49wP37+TZAUWnH0KH8wfH0KU9u9g6XL/O0N/v3ERTEVU0CwseN09F3vDF69NBgv5Wd\nOxE9aAsirP8PaS8NYSx5gRpO/8L+/oYCV9Y+f84/zo4dPN+bRAI0rpqOuw8laE+x8GMVUL+GHHej\njZDlCq1fnz8EOnTgrxWVPJtU3QsiIDw8R+z9/fn+OWZmQKdOfO+aLl2AK5viMWqRBb5UROIfiWOe\n30lmZiYuXryI48eP4/jx47h69SoAoGLFiujatSt8fZvB6uE92HS7gTNuZ9DpSie8ONoYae0cMWWF\nBQIeByDgUQAevX4EADA3MkfrKq3Rzqkd2jm1Q3PH5vh1hQz7E75AhRAJegb1xKFWhxDTSIE+FscK\nTGH0LkuWAE4volF+w1UYvzFGWrk0vB77ER7b2mvVhq52qEKZL1sVhZ2jKwwdOnSAl5cXmjbN61Z3\nd3eHubk5fvjhhyLpVxlF6fMvc7l9FJkKij8TT+HjwulchXPkC1/yN/WnkK9DKGZ/DGUmZdLzHc/J\n39SffOGbffib+tPzHc+LxCYKCeH5Z1avzj6Vlka0eDGRoSG/ZGVF5OlJ9OSJkvoREbzguHGa9xkd\nTWRkRPTtt3lOKxRER48SdejA+y1fnuibb3ganKFD+W6SlSrxawBR06Z824EbNwpIkaNQ8MasrHLy\n/584ka9YQgJPXdS5M5FEwou5uREtX060bx+RrS2Rjw8v6+PD3586RXTxIr9PXbvyLNRZdtWrx2/H\nnj1Ez57xMln1szh6lH+m8eOJqlXLqVu/PtG0abz93JmqfXyIbGwUNGDAJrK0tKQBAzaRtXUmTZ9+\nnPr160eWlpYEgKRSKbVt25Y8PT3p2rVrpHh7Y4zmGxHcUeBRYWkF6v1nb1oZtJKuRF2hDHmG5r/P\nMkZpzu3z8ccf03///Zfv/Lx582jp0qXFaovI5/8Oz3c8p8CqgeTLfCmwaiA9++MZxfsoEfx+OYKv\nro0iE34ivpmIVMoFmYhOniSqW5fffSMjou7dcx4CEgkXun373kmhP2ECz0t//75mfWZtFBMWRkT8\nYbNlC1HDhvx05cpEy5bxfGjKhNfbm2jhQqIWLXJEs3p1nkfN15co461uLV5M5OP9gH8+gMjZmXzO\nKGjxYl7m2DG+fYCpKb9crRrR7NlEuf+mFy8mOno0mWbOnEmWlpY0a9YsOnYshRYvzvuRMjKILl0i\nWrKE56bL/TCoUoXIxIRo1iz+uT76KOeamRlRjx5E69YRPXxY8C0bO/Y+lSvXk4yNjQkAMcYI6EDA\nj+Tg4EDDhw+nvXv30suXL5XWj3wdSa02tSLMeyv480DVVlSj5YHL6c6LO9kPCUHpFv+ionnz5tl7\nBmcdN27cUFlHiH8ulI3ac4/eQ/qFUPS+aKWCXyLI5URVqxJ16UIPHxJ99RW/65Uq8X1NcouulRXR\noEFcmAEiGxuiyZOJrl8noqgoPiwfPFh9n+npvIPOnenVKy6uWaP5xo35CDw9nRdVNmL28aE8wvvs\nGdHGjTypp7FxzjeVQYOI5s0jsimfTj6STkQAnTHoTJbmGdSrF5GdXU7ZceN4ck9l+ufv7082NjZk\nampKAEgmk5GNjQ35+/ur/Ji5HwbduvHbkyX4UilR375Ep09rvg/NgAEDlG4U/sUXX6gV7mcJz6jX\nnl7Zo3yjBUYk+VlC44+M16zzD4wPUfwLgxD/XARWDVQq/AG2AZSZWEoEPzcBAZQCY5rfO5hMTLhA\neXoSeXgULLqZmdxz0q8f/2aQ5SJZ++lBeglL7kZSxZ499ASO9MNX97JHx5068TZ1HXwmJBAdOEA0\nZAh3FQFEBpJMMkQatcJ5kiKDAP6Q6NOH6O+/+bcOVQwaNEip6A4aNEgr2zIyiEaP5jbNmaPd54qN\njSV7e3ut7VAoFLQteBtZLbIi4wXG1HBtQxr3zzgKfhZME45MoF57emlnyAeCEH/NEOKfC1+WX/h9\n4Uu+zLdQ7RUlCgXR4S5rqQa7TwAfiT56pF0bL17wqYImTfhvywTJNKCKP50+zdPjv/sA+f13oo/M\nwsgA6SSVKqh/f6IrV1T3kZiYmMflkpSUpJFtGRl8W9ypX0eSJeIJIKqKB7R51l169Urzz6gv8c9y\nWc2Zk9eVpY5r165R1apVSSKRaGXHo1ePqMuOLgR3UJvNbeh27G2t7P2QEeKvGUL83/Lm8hvylSgX\n/8CqgVq3V5TcuUP0RRc5n2As94ROn9a9zatXiSY2v0hWiCOA78tiakq0axfRmTNEzZrlPCAmt79K\nDx6ob7OwLpfc+PgQ2Vqm0/+1DyRby3SNRTeLLl26KBXdzz//XDsblMxdqLNl9+7dJJPJqHLlyrR+\n/XqysbEhmUym8l7IFXJa/996slhoQaaeprT6wmqSK+TafegPHCH+miHEn4jiz8TTWYuzdNbmLPmb\nFGOkjpYkJvJJRyMjIgtZOi3DVEr/+6j+Onj9mlKsK9Huxp7UuTNl+7gBIsaIRtTypzjTyqTp0FvX\nUXdhRTf02YjdAAAgAElEQVSLgIAAkslkJJFIyMTEhACQsbExSSQSYozRrFmzKE2d34g0m7vITWZm\nJk2fPp0AUNu2ben5c/73k5SURLNmzSJLS0uaPXt2vm9B9+LuUYetHQjuoE7bOlFEfIRmH1SQByH+\nmvHBi3/M/hjyM/Kjiw0vUurT1OKN1FGCMqE5c4Zo4MCcydpBg4iieozlSpg1u/oOhXW3kJcX78TP\njx49IvrkE/525uRE7myfMEHjz9K3b1+dxF9b0c3NmTNnyNTUlOrUqUN3797NI7rPnj2jESNGEABy\ndXWlW7duafyZ1BEXF0edO3cmADRhwgSNHi6Z8kxaHricZB4yKvdLOdp0ZZOI3tEBIf6a8UGLf+SG\nSPKV+NKV1lcoPU65iBY3745uvb1zQjWdnYnOniWiN2/47G4BQqyTuyU5mYfvtGlDPmcUOX5u0yTy\nQYe8cZQqSEtLowoVKigV/z59+mh4NwrHiRMnyMTEhBo2bEjPnj0rsNxff/1Ftra2ZGJiQqtWrSK5\nXDf3ys2bN6lmzZpkaGhImzZt0qhOaEwotfy9JcEd1H1Xd3ryWtliDIE2lAbxl0gk1KRJE2rYsCH1\n6dNH88HXW/z9/cnV1ZWkUint27evSGz8IMVfoVDQQ4+H5Atfuv7F9dITuvmWrAdAVhy8mRnRmjU5\n8e+0bRu/cP680vo6T3KuW0c+6EC25dL4Qyg9nXxs+5Kt4UuNXC4KhYKGDRtGAMjc3Dzbz21kZEQA\nyN7enoKDgzWzRUsOHz5MRkZG5OLiQrGxsWrLP3/+nLp3704A6NNPP6UnSlfCqefAgQNkZmZGDg4O\nFBhY8BxR1Jsoar+lPT1+9ZgWnl1IRguMyHqxNe24vkOM9vWENuKvy7dLVZiZmWX/PGDAAFq2bJlW\n9R88eEDXr1+nwYMHC/HX5lAl/gq5gu5MukO+8KXQQaEkTy+dk2lubvwOurgQxcS8c7FzZ74qqgCx\n0Fn809JosdUv5FNrNF9LsHcvEUA+Huc1+qfw9PQkADRv3rx8fu6AgABydHQkU1NT2r9/v2b2aMj+\n/fvJwMCAmjVrRnFxcRrXUygUtHHjRjIzMyNLS0vatWuXxnXlcjnNnj2bAFCLFi0oMjJSZfnxR8YT\nc2dks9iG4A7qu7cvPU8oHXNKZQVtxF/XeaWCyC3+69ato/Hj+ZqMbdu2UePGjcnZ2Vmj/8ehQ4cK\n8dfmKEj85WlyujXgFvnCl+5OuUsKeekcaWUtoG3ZUskf4rNnfKnu7NkF1tdLeOMff3Aj9u0jatuW\nP2wy1X9D2rNnDwGggQMHFjiSjYqKohYtWhAAcnd319ndQkS0a9cukkql1Lp1a3qlTSxoLu7evUst\nW7YkAPTNN99QfHy8yvKvXr2ibt26EQAaMWIEpapY8WXiYaI0LYOJh0mhbBUUTG5RmzyZ6OOPVR/O\nzty16uTEX52dVZefPFm9DVnin5GRQT169KDffvuNQkJCqHbt2tnfSDUZoLyv4l+qNnCXJ8kR0jME\nMbtiUP2X6qi5vCbPXV/K2L8fmDsXqFkT8PMD9u7le4b7+r4t8OeffGslFduyOTvz7IgGBjyxalYG\nQFNTU80NGTCAb14+dixw7hzQvTugJpNgUFAQhg4dirZt22Lz5s35MpZm4eDgAD8/PwwZMgTu7u7o\n168fkpKSNLftHbZu3YqBAweiXbt2OHnyZHaKXG2pVasWAgIC4OHhgf3796Nx48Y4ffq00rK3b99G\n8+bNcfLkSaxduxa///47jI2NC2w7YlIEnO1zslbKDGQY2HggHkx+UChbBfrDygpwcAAeP+avVla6\nt5m1fWLTpk3h5OSEkSNHwsfHB3379oWtrS0AwNpaSaLFsoI+niCFOd4d+afHpdOVllfIV+JLkZtU\nfy0vSeRyotq1+egj90M5jw+yWTMiV9cC24iJiSF7e3uqX78+TZ8+nSwtLWnmzJnUp08fAkBz587V\n3Le8cCFlx3rKZEQqfNn3798nOzs7qlmzpka+diLubvHy8sqeHHuoKjlOAWzYsIEA0GeffaZyUi3L\n1/4soeAJ4NxcvnyZ6tWrRwBo0qRJFBsbmx091bdvXzI3N6cKFSpovGbBJ8Inz2hfpGcoOrSd8C3s\nAj5V5Hb7ZLF69WqaNWuWVu28ryP/UiH+KU9S6GKDi+Rn7EcxB991npcuVq7kd+233woocOcOL+Dl\npfSyQqGgL7/8koyMjOj69et5rmVmZmaHN37//feaPQA8PXPEXyrlDwMlvHz5kurVq0dWVlYUHh6u\nvt13OHbsGJUrV47s7OwoICBA43qrVq0iANStWzdKSUlRWXb8kfFaC25ycjJNmjSJAJ5tMyspW9Z7\nTf8pnyU8I/ul9mS+0JxGHR4l0jMUMaXN559FltvnxYsXRFS23T4lLv5Jt5Mo0CmQzlqcpXhf1f7b\nkub69ZwsnAXq8rx5fLXV06dKL69bt44A0IoVK5Rel8vlNHHiRAJA48ePV+9rDwzkI36ptMCRf3p6\nOnXq1IkMDQ3Jz89PdXsqCAsLo9q1a2scJrlkyRICQL169VIZS68PX3unTp0KPYeSIc+gDls7kMxD\nRjejb2rcp6DwlLZon9xs3bqVGjZsSM7OzjR06NAC61+6dCk7MMLa2poaNGigm0FKKJPiXwd16FzF\nc7TCJJjGmz2gN1ff6ONeFRnJyUSNGvGUCm8zM+dHoSCqVYuvulJCaGgoyWQy6ty5s0pRVygU2atP\nhw4dShkZavK+BwbyEb8S4VcoFDRy5EgCQNu2bVPdjgbEx8dnL5D67rvvCrRt/vz52ZOy6QUscssi\n6k0Udd3RNVv0pT9LacCBARq7f4h0m0CffWY2wR209dpWjfsT6EZpiPN/Hyiz4r8c16g80mj3+NIf\nRjdpEr9bx4+rKHTxIi/0++/5LqWlpZGrqyvZ2NhQVFSU2v4UCkW2gPbr10+tgBbE4sWLCQD99NNP\nhaqvjIyMDJo2bRoBoE6dOuX5aqxQKLLDKocOHUqZGkQfpWSkkNUiq2zhhzvIbYN26T8KK/7H7hwj\nuINGHhqpVX8C3RDirxllUvyN4EoWSKfluFbqkrK9y7Fj/E5NmqSm4KRJ3C+kZLOPrJH833//rVXf\nXl5eBIC+/PJLtT7zd9m/f3/26Fvd/IG2k61ERN7e3mRkZETVq1en0aNHk6WlJTVv3pwA0OjRozUO\nD51yfApfPbuzO12Lukb1fq1HcAetvrBafeW3ZK2YVpeULTePXz0m68XW5LzOmZLTkzXuS6A775P4\ne3h45NuExcPDo1j6LpPiD7iRBHIahId0FGf1cZ+KhOho7upp1IhIpfZmZBBVqMB3a3mHM2fOEGOM\nxowZUygb1q5dq1G0TG4uXrxIJiYm1Lp1a40eGiMPjSxUdMvatWvf7niVM9o2MTHReG7h33v/EtxB\nE49OzD6XIc+gnrt7EnNntPvmbo1tUZeULTdpmWnU8veWZLHQgu68uKNxHwL98D6Jf0lSJsXfEo3J\nEDzlsbUkjTZu1Gh9UrGiUPDJXWNjvoetSk6c4LfzwIE8p+Pi4sjR0ZHq1KlDiYmJhbZly5YtJJFI\nqF27dvT69WuVZR8+fEj29vZUvXp1ism39Dgvuk626uJrj0uOo0rLKlG9NfUoKT2vUCenJ1M773Zk\nON+QTt47qZEt2jD1xFSCO2hvyF69ty1QjxB/zSiTi7wqIA2LcQPmyICdA8OYMYCrK3DqVElZlJ/1\n64EjR4DFi4HGjdUU3rkTKF8e+OKL7FNEhDFjxiA6Ohq7du2CmZlZoW0ZNmwYdu3ahaCgIHz22WeI\nj49XWu7169fo1q0bUlNTcfToUdjZ2alsN3RCKCyMLLLfG0oMi2VhExFh3JFxiEmKwc6vdsLUMO/i\nNpmhDIf7H0Z9u/r46s+vcCnykt76Phh2ECsurMB3zb9D34Z99dauQPA+UaIrfFtWTcHWWQkY/p0h\n9u0DEhOBzp2Bbt2A0NCStAwICwOmTQM+/xz47js1hZOTgb/+Avr0AUxMsk9v3boVBw4cgIeHB9zc\n3HS26euvv8aBAwcQHByMTz75BDExMXmuZ2RkoF+/fggPD8fBgwdRv359tW1uvLIRCekJYGCQMAky\nFBmIS45DRfOKOturih03dmBf6D7M7zAfHzl8pLSMpYklTgw8gQpmFfDFzi8Q/iJc537vx9/H8EPD\n0dyxObw6e+ncnkDw3qKPrw+FOZTl9klNJVq6lKh8eR62Pn68irDKIiQ1lSdrs7Xl+6arZc8e7vLJ\nFYx89+5dMjMzow4dOqiMeCnMROvJkydJJpNRvXr16M6dO9mrWl1dXQkAbd68WaN2Lj29RJKfJVR1\nRVWacGQCBT0JIrsldiT5WUKXnl7SqI3CTLQ+ePmAyv1Sjtp6t6VMuXpf350Xd8huiR05rXCip6+V\nr5/QhJSMFHJd70pWi6zo4cuHhW5HoDvC7aMZZdLnryqrZ2ws0cSJ/AFQrhzfq1bLQBed+PFHfmcO\nHdKwwpdfEjk68twPxBdVNW/enCwtLenx48cqqxZmVSsRF92sHbCyhDdLfDVJZ5CakUoN1zakSssq\n0cuUnOik5wnPqdrKamS/1J4evHygkS3aTLRmyjOpnXc7slhooXH7RERXoq6QxUILari2IcUla54N\nNDdjDo8huIOOhB8pVH2B/iiM+BdmoKQKXfP5p6amUr9+/ahmzZrUvHlzeqDJvqla8sGJfxZhYVxX\nAaKqVfkAW9mm5fpY7ZfF6dO8v3HjNKzw4gWRgQHRDz9kn/rpp58IAO3dW/Bkoj5WtRa0960mk60/\nnfmpQCEMjQkly0WWVH9N/TwPBn2wKGBRoRdUnYk4Q0YLjKj15tb5JojVsf36doI7aMapGVr3K9A/\nhRH/wg6UCkLXfP5r166lsWPHEhHfC7pfv356sSs3H6z4Z3H6NFGTJtza+vWJLC31n+eDiOu4oyNR\n3bpEGg8C1q3jhl27RkREZ8+eJYlEQsOGDVNZLepNFPXf358k7hKCO8h4gTENPDCwWFa1Xom6QtKf\npTTkryEFlvF94EuG8w2p49aOlJapfptDTbgadZUM5xtSn719Cr0pyr5b+4i5M+q2sxulZ2q28O1W\nzC0y9TSl9lvaU4ZczWppQbGQJ6Xz8cn08ZaPCzwkP0uUDpQkP0sKrDP5uPqczrrm8+/cuXP2pkAZ\nGRlkY2Oj981+ymS0jzZ06gRcuQJ4ewOvXvGja1dg/HieSnnvXqBjR936IOKZkWNigF27AI0yKwcF\nAYsWAdWqAU2a4NWrVxg0aBCqVauG1atXq6zqYOGAR68eQQEFACBNngY5yYt8ojVdno7hh4bDzswO\nKz5fUWC5DtU6wLunN3wf+mLU4VF8pKADKRkpGHhwIGxNbbG+2/oCU0mro0+DPlj7xVocvXsUo/8Z\nrdaupPQk9NnbB+ZG5tjdezcMJAaF6ldQcjSv1BwVTCtAwrhcSZgEFcwqoIVjC720n5mZiePHj6Nx\n48a4desWPDw84OPjg+vXr2PVqlUF1ouMjESVKlUA8NTs5cuXR1xcnF5sKhb08QQpzKHNyD83CQlE\nc+dyTwtAZGfHc6ndvKki2ZoGbN7M21uyRMMKgYFEJia8kqEhUWAgDRgwgKRSKQUFBamt/uDlA5L8\nLCGn5U50NPwomSwwIVNPU4pJ1DyraWEmW9193QnuoEO3NZvQmO83n+AOmuc7T2O7lDHp2CSCO/QW\ns5/1OX7898cCyygUChp0cBAxd0an75/WS78C/aCt22fcP+NI8rNEr6m2s3z+TZo0oYkTJ1JaWppW\nKZ0bNmyYZ0vRGjVqaJwqXVM+eLfPu/j4EFlbE3XpkrNxOkBUpw7RrFlEV69q9yC4c4fvwfvJJ9lz\ntupZuJBn7wSIpFLa0a8fAaD58+errapQKKjz9s5kvtCcHr16REREQU+CyMTDhFpvbk0pGZrPbmsz\n2Rr8LJgM5hvQgAMDNG5foVDQsL+H6ZT47OS9kwR30KRj6vJjaI5CoaAJRyYQ3EFLzy9VWmbj5Y0E\nd9B8P/W/E0Hxoq3499rTiyYcmaDXVNu65vN/390+7534K8vtbW1NNGUKUadOPEIIIKpRg2j6dJ5r\nTdXvIz2d771iZUWk1b7ggYHZnT0wNqZyZmbUpk0b9Rk4iWhb8DaCO2jNxTV5zu+7tY/gDvp639ck\nV+h33+L0zHRyXe9KFZZWoBdJL7Su++kfn5LBfAOtR9Avkl6Qg5cDNVjbQO/5czLlmdR3b1+CO2hb\ncN6MpVejrpLxAmPqvL2z3u+lQHdKQ6inrvn816xZk2fCt2/fvnq3UYh/LtTl9o6N5Uk1u3TJcQ05\nORFNnUp0/jwf2eduY/ZsXmbuXO0ihhITEmimTEaWEgk52tmRhYWFRqFe0YnRZL3YmtpsbqNUlBaf\nW0xwB80+U/D+v4XBw9+D4A46EHpAfWElvEp5RQ3XNqTyv5SnkOgQjeooFArq/WdvMpxvSFejrhaq\nX3WkZqRSp22dSPqzlI6EH6GoN1HUenNrqrqiKjkuc9TKjSYoPkqr+BNpns8/JSWF+vTpQzVr1qRm\nzZrR/fv39W6jEP9CEh9PtHUrDxc1MuKftlIlov/9jy8kW76ce266dNEuYsjf359sLC3JNFeEjbm5\nuUbx9V/v+5qMFhhRWGyY0usKhYJGHx5NcAd5X/XW5uMWyM3om2Q435D67dMtFO3Rq0dU0asiOa1w\n0igqaeu1rQR30KKARTr1q443qW/IbYMbyTxk9L/d/8uOBjn36FyR9isoPKVB/N8HhPjrgdeviXbu\nJOrVK2eeFuDuHhsb7UJFCxtiefj2YYI7aIH/ApXl0jPT6bM/PiOD+QZ0JuKM5oYpIUOeQU03NiXb\nJbZ6GQVfibpCZp5m5LbBjRLTCk5UFxEfQRYLLaiddzuNVvHqivECY53XTQiKDyH+mvHBh3rqg3Ll\ngAEDgIMHgdhYHh7q4gK8fAlMmKB7qKg6Xqe+xvij49G4QmNMbzNdZVlDqSH29d2HujZ18dWfXyEs\nNqzQ/S4LXIbLUZex9ou1sDNTneRNEz5y+Ah7+uzBtefX0P9Af8gV8nxl5Ao5Bv81GIwx/NHrD0gl\nUp37VceDyQ/Qo04PMPAQUlMD02JJUCco+3h6esLFxSXP4enpWdJm6YxG4s8Y68IYC2eM3WOMzSig\nTAfGWDBj7BZjzF+/ZuoXc3PA1hZ4+hSYMwdYtw7w9S3aPv/v9P/hWeIzbO6xGUZSI7Xly5uUx9EB\nR2FiYIIvdn2B6MRorfsMiw3DXL+5+Kr+V+jbQH/ZK7vX6Y5fu/6Kf+78g8knJvOvkLlYcn4Jzj85\njzVd16CaZTW99asKBwsHVLKoBMYYTAxMkCpPRTnjckW+bkJQ9pk9ezaCg4PzHLNnzy5ps3RH3VcD\nAFIA9wHUAGAE4DqABu+UsQQQCsDp7fsK6totbrdPbpRFDGnl8z94kIxyuXvUxdf7PfAjuIOmnZim\nta2Xnl4imYeMWmxqoVW0TKY8k1psakHWi63peULRbJP5/cnvCe6g5YHLs89dibpCBvMNqN++fnoP\ne1NHUYQDCoqG0NDQYv/7eN9QKBQl6/MH0ArAyVzvZwKY+U6ZCQA8tOm4JMVfXcSQOu79+isxgJo3\naKA2vj45PZlqr65N1VdWV+kjV8XB0IPE3Bn12dtH47DFpeeXEtxBO2/sLFSfmiBXyKn3n72JuTP6\n/crv1HZzW6q1uhY5LnMsdPI1wYdBREQExcbGigdAASgUCoqNjaWIiIh81/Ql/pqsdXcE8CTX+6cA\n3l1XXQeAIWPMD4AFgFVE9IcWX0CKlelKXO4dO2ru91+8fj0MAfx9/DgcnJxUlp3vPx934+/i1OBT\nMDMq3GYuver3gldnL3z/7/eYdWYWFn26SGX5O3F3MMd3DnrU7YH+jfoXqk9NkDAJtvfajsiESIw9\nMhZy4v7/U4NPwVpmXWT9Ct5/KleujKdPnyI2NrakTSm1mJiYoHLlykXWvr4SnRgAcAPQCYAMQBBj\n7AIR3cldiDE2BsAYAHBSI5qllcjISGwNDcVIR0e1wn/t2TUsDVyK4S7D8WmNT3Xqd2rLqbgXfw+L\nzy9GTauaGO02Wmk5uUKOEYdGwMTARKccOppivcQaqZmpec59tv0zmBiYIGV2SpH2LXh/MTQ0RPXq\n1UvajA8aTSZ8IwFUyfW+8ttzuXkK7hpKIqIXAM4CaPJuQ0S0kYiaElFTddsLllaWLVwIBRGm9+6t\nslymIhOj/hkFOzM7LOu8TOd+GWNY3XU1utTqgvFHx+PUfeX7Xf566Vecf3Ieq7qsgoOFg879qiNi\nUgQGNBoAEwO+g5nMQCaibASC9wBNxP8/ALUZY9UZY0YAvgFw+J0yhwC0ZYwZMMZMwd1ChY9PLKW8\nePECGzZvxgAA1Xv2VFl2edByXH12FWu6roGVzEov/RtIDPBnnz/RsEJD9NnXByExIXmu34u/h1ln\nZuGL2l9gsPNgvfSpDgcLB5QzLod0eTpMDEyQJk8TUTYCwXuAWvEnokwAEwGcBBf0vUR0izE2jjE2\n7m2ZMAAnANwAcAnA70QUUlCb7yurVq1CcloaZkilQMuWBZa7G3cX8/zmoVe9XujdQPU3BG0pZ1wO\nR/ofgZmhGbrt6obnic8BAApSYOThkTCSGmFj941F7u7JTXRSNMa5jcOFkRcwzm1ctk0CgaD0wuid\nGO3iomnTpnT58uUS6bswvHnzBk5OTuhkYIADtWoBFy4oLacgBT7Z9gmCnwcj9NtQVLKoVCT2XH12\nFe22tEMDuwbY02cPPt/+Oe6/vI/NPTZjhOuIIulTIBCUPIyxK0TUVNd2PpgVvrry22+/4fXr15j5\n+jXQrl2B5X6/+jv8H/nDq7NXkQk/8Halbe89uBJ1BR22dMD9l/dRpVwVDHcZXmR9CgSCsoMY+WtA\ncnIyqlWrBtdq1XDyv/+AQ4eAHj3ylYt8E4kGvzWAm4Mbzgw5U+SuF5mnLF+kDQARaSMQlGHe+5F/\n1hLp5OTkkjJBY7y9vREbG4vZzs78RJs2+coQEb499i3S5enY+GXx+NyzIm0MJYYARD4bgUCgOSUm\n/nK5HCtWrICTkxPOnj1bUmaoJT09HUuWLEGbNm3Q7skToFEjwMYmX7n9oftxKPwQ5neYj1rWtYrF\ntqxIGznJRT4bgUCgFSXq809JSUFcXBw2bdpUkmaoZOfOnXjy5Almz5gBFhSk1N8fnxKPiccnws3B\nDVNbTS1W+0SkjUAgKAz6WuFbJpHL5Vi0aBFcXFzQpWJFICEhj/g/S3iGbw58g4rmFRGXHIeTg07C\nQFK8t/Tg1wezf17bbW2x9i0QCN5fSoX4p6bmn7QsDRw4cAB37tzB3r17wc6d4ydzif+CswsQ8CgA\nBMLMtjPhUtGlhCwVCAQC7SixaB/GGBkZGSE9PR12dnY4cuQImjdvXiK2KIOI4OrqitTUVNy6dQvS\nfv2Aq1eBBw9ElI1AICgx3vtoH6lUih9//BHnz5+HmZkZ2rVrh40bN+bbGKSkOH78OK5fv44ZM2ZA\nKpEAAQHZo/6ISRHo36g/pIzvUGUsNRZRNgKB4L2ixMTfxcUFHh4eaN26Na5cuYKOHTti7NixGDVq\nVIm7gYgInp6ecHJywsCBA4E7d/jej+3bA+BRNiExIZCTHAYSA2QoMkSUjUAgeK8oFSt8ra2tcfTo\nUfz000/w9vZGu3bt8OjRoxKz5+zZswgMDMT06dNhaGjIR/1A9sh/1YVVuBlzE84VnHF59GURZSMQ\nCN47St0K38OHD2Pw4MEwNDTEnj178OmnuuXBLwyff/45rl+/jgcPHkAmkwFDhgAnTwLPn+Ov23+j\n997e6FW/F/b22Vssm5MLBAJBFu+9z78gevTogcuXL8PBwQGff/45Fi1aVKzzAP/99x/+/fdfTJs2\njQs/wEf+bdviQuRFDDg4AC0qt8COXjuE8AsEgveWUif+AFC7dm1cuHAB/fr1w8yZM9G7d2+8efOm\nWPr+5ZdfYGlpiXHjxvETT58CDx/ifuv6+HL3l3C0cMThbw5DZigrFnsEAoGgKCg58X/+HAgKKvCy\nmZkZdu3aheXLl+Pw4cNo3rw5wsKKdn+Y0NBQ/PXXX/juu+9Qrlw5fjIgAC9Mga60HUSEYwOPwc7s\n/dyFTCAQCLIoOfGPjAQ6dVL5AGCMYerUqThz5gxevnyJ5s2bY//+/UVm0i+//AIzMzNMnjw5+1xK\ngC96DpTgcWo0Dvc/jDo2dYqsf4FAICguSkz8w22A5wapgJ+f2rIff/wxrl69ikaNGqFv376YMmUK\nZsyYASsrK71lBo2IiMDu3bsxduxY2LxN3KYgBYam/4mgSgrs+GoHWldprXM/AoFAUCogohI54AAa\n3w1Ep06RpqSmplLPnj0JAEkkEgJAMpmMbGxsyN/fX+N2lDFu3DgyMjKiyMjI7HM/HPqW4A7ymt9F\np7YFAoFAXwC4THrQ4BKd8F3XDGDnP4OJh4lG5Y2NjWFhYQEAUCgUAHIyg27cuLHQdkRFRcHb2xvD\nhw9HpUp89621l9bC69paTLwITGs/o9BtCwQCQWmkRMWfvY3glJIEow6PQuCTwEKHdf7111+YM2cO\n7t+/r3Xd5cuXQy6XY/r06QCAw+GHMenEJPTIrImVPoZgLVoUyiaBQCAorZSY+DPGwJgE/3tggm/u\nG2NPyB608W6D+mvrY8n5JXiW8Eyr9sqXLw9PT0/UqlULHTt2xPbt2zWaC4iLi8P69evRv39/1KhR\nA/9F/odv9n8DNwc37PKxgrRZC8BEs28mAoFA8L5QYuJf37Y+xjUdB+bqis3bXuF55hR49/CGnZkd\n/u/0/6HKiirosbsH/r79NzLkGdn1Ro8eDRsbm+wFWDKZDDY2NtizZw8ePXoEDw8PPH78GEOGDIGD\ngwPGjh2LixcvFviNYvXq1UhKSsKMGTPw4OUDdN/dHRXNK+KfnntgdilY5WbtAoFA8N6ij4mDwhxu\nbmJm3FUAAA84SURBVG45Mxhff01kZEQUFkZEROEvwmnGqRnk4OVAcAfZLbGj709+TyHRIURElJSU\nRJNmTSLpKClNnj2ZkpKS8kyIyOVy8vPzoyFDhpBMJiMA1KBBA/Ly8qLnz58TEVFiYiJNmzaNGGNU\np04devLiCdX9tS5ZLbKisNgwotOniQCiY8cKNSkjEAgERQH0NOFbOnL7REcD9evz/XH9/AAJ/0KS\nqcjEyXsn4R3sjcPhh5GpyEQLxxYY4ToCF59exNbrWzHWbSx+6/Zbgf28efMGf/75J7y9vXHhwgUY\nGBigZcuWuH79OlJTU5GRkQEjUyMoBirAKjOcGXoG7aq2A9zdgfnzgZcvgfLli/6GCAQCgQboK7dP\n6RB/APD2BkaOBDZuBEaPzlc+NikWO27swPf/fg9CfpuNpEZ4OPkhKppXBGNMaZ9hYWHYsmULVq9e\njbS0NH6SAfgKQGOg7fO2CFj3NoNnp05AfDxw7ZqOn1QgEAj0R9kTfyIuuFevAmFhgIOD0npRb6Iw\n4vAInIk4g0zKzHfdwsgC9WzroZ5tPdS1qZv9cy3rWjA2MAYADBw4ELt27QLMAYwCYAngFDCo+iBs\n374dyMgALC35w2j1av1/eIFAICgk+hL/UrGHLwCAMWDDBqBxY2DSJGDfPqXFKpWrhOqW1aGAAiYG\nJkiXp2Ng44EY0mQIwl+E4/aL27gddxu+D32x/cb27HoSJkF1y+qoZ1sPEXYRwEfghyWAGADnAVR/\nW/jqVSA5OXvzFoFAIChrlB7xB4DatYG5c4HZs4HDh4EePZQWi06Kxji3cRjjNgYbr2zEs8Rn+LTG\np/i0Rt7c/4npibgTd4c/EF7cRngcfziEWYUBuZuuAMAd2CvZi+3YDpw9y8+LSB+BQFBGKT1unywy\nMgA3N+5vDw0FsrJr6pHIN5EYe3gsTtw7ATmTw4AM0LtBb6z8YiXfirFHD+D2bb59o0AgEJQiyuxm\nLjA0BDZtAqKigFmziqQLx3KOqGJZBSQhmBiYQCFRwNrMmgu/QgGcOydG/QKBoExT+sQfAFq0AL77\nDvjtN5Upn3Uhy3V0YeSFvHvwhoby8E4h/gKBoAxT+tw+WSQkAA0bcrfP1auAkVHxGLZuHTBhAnD/\nPlCjRvH0KRAIBBpSdt0+WVhYcCG+dQtYsqT4+g0IACpVAqpXV19WIBAI3lNKr/gDQLduwNdfAwsW\nAOHhRd8fEY/0adeOh54KBAJBGUUj8WeMdWGMhTPG7jHGCkxuzxhrxhjLZIz10ZuFq1YBpqbAmDF8\nMrYoefiQby8p/P0CgaCMo1b8GWNSAGsBdAXQAEB/xliDAsotBvCvXi20twe8vPiIfPNmvTadj4C3\nqR2E+AsEgjKOJiP/5gDuEVEEEaUD2AOgp5Jy3wE4AL5eVr+MGAF06AD8+CPwTLs8/1oREMDTOjRq\nVHR9CAQCQSlAE/F3BPAk1/unb89lwxhzBNALwDr9mZanA57wLTWVp34oKgICgLZts7OKCgQCQVlF\nXyq3EsD/EZFKpzxjbAxj7DJj7HJsbKx2PWSlfti/n6d+0DfR0XxSWbh8BALBB4Am4h8JoEqu95Xf\nnstNUwB7GGMPAfQB8Btj7H/vNkREG4moKRE1tbOz097aH3/kid8mTADevNG+virOneOvQvwFAsEH\ngCaJ3f4DUJsxVh1c9L8BMCB3ASLKDopnjG0FcISI/tajnZys1A+tWvF5ADc3PhfQqpXubQcEADIZ\nb1MgEAjKOGrFn4gyGWMTAZwEIAXgTUS3GGPj3l5fX8Q25qVFC6BPH57y+a+/AGNj4MwZ3R8AAQG8\n7eJaSSwQCAQliEYpnYnoGIBj75xTKvpENEx3s9TQ4G2kqUIBpKXxrR91Ef83b4DgYJ5KWiAQCD4A\n3s+wls8/B0xM+M8KBZ+olcsL315QEG9HbN4iEAg+EErXZi6a0qoV4OMDnDoF/PcfsG0b8OQJsGsX\nXxSmLWfPAlIp0LKl/m0VCASCUsj7Kf4AfwBkuXq2bOERQK6uwJ9/ah+xExAAfPQRYG6ufzsFAoGg\nFPJ+un3eZfjw/2/v/kPvqus4jj/fbPrPEk03zHQrBREWLZMv4sZaE0PcCGf+IZqUljAGLtofEQNB\nB/1lUQPDHKtGJpIaaQ3ZNI1UcGxuzjld/trM0DEnWc5i4Jq9++Oc5d313u/uV8/98d3n+YAv33PP\n+dzvee/z/ez1Pfecez4XtmyBadPg4our6SB6nar6vffgqad8i6ekohwf4Q8wZw5s2wZXXFHdD3Dl\nlfDOO8d+3tat1R8Aw19SQY6f8Ac4+eTqLaCrV8ODD1bv2X/mmfGfc2Qyt/nz+1+fJI2I4yv8oZoH\naMUKePzx6oh+7tzqxrBup4GeeKJ66+j06YOtU5KG6PgL/yPmzauO+hcsqD4L4Prr4eDBo9u8/z5s\n2uQpH0nFOX7DH2DGDNi4EW65Be66q7qDt/UTwXburG7wMvwlFeb4Dn+o3r+/ahU89FD1WQBjY3Df\nfdU2P7xFUqEm7/v8J+rSS6vTQFddVX0u8JNPwvbt1UXivXth1qxhVyhJA3P8H/m3mjmzuhC8YgXc\ndls1jfOBA3DJJdUUD5JUiLLCH6pZO1evhq+3zEp96FA1OZwkFaK88D9i+fJq/v4pU6o/CAsXDrsi\nSRqYcs75t5s7t/ocgMcea+4DYSRpkig3/OHoyeEkqSDlnvaRpIIZ/pJUIMNfkgpk+EtSgQx/SSqQ\n4S9JBTL8JalAhr8kFcjwl6QCGf6SVCDDX5IKZPhLUoEMf0kqkOEvSQUy/CWpQIa/JBXI8JekAhn+\nklSgnsI/Ii6LiJciYndErOyw/dqI2BkRz0XEpoj4QvOlSpKacszwj4gpwO3AImA2cE1EzG5r9lfg\ny5n5eeAHwNqmC5UkNaeXI/8Lgd2Z+WpmHgLuAZa0NsjMTZn5z/rhZuCsZsuUJDWpl/A/E3i95fEb\n9bpubgA2fpyiJEn9NbXJHxYRF1OF//wu25cCSwFmzZrV5K4lSRPQy5H/XmBmy+Oz6nVHiYg5wC+A\nJZn5dqcflJlrM3MsM8dmzJjxUeqVJDWgl/DfCpwbEWdHxInA1cD61gYRMQu4H/hGZr7cfJmSpCYd\n87RPZh6OiOXAw8AUYF1m7oqIZfX2NcDNwGnAzyIC4HBmjvWvbEnSxxGZOZQdj42N5bZt24ayb0ma\nrCLi6SYOrr3DV5IKZPhLUoEMf0kqkOEvSQUy/CWpQIa/JBXI8JekAhn+klQgw1+SCmT4S1KBDH9J\nKpDhL0kFMvwlqUCGvyQVyPCXpAIZ/pJUIMNfkgpk+EtSgQx/SSqQ4S9JBTL8JalAhr8kFcjwl6QC\nGf6SVCDDX5IKZPhLUoEMf0kqkOEvSQUy/CWpQIa/JBXI8JekAhn+klQgw1+SCmT4S1KBegr/iLgs\nIl6KiN0RsbLD9oiI2+rtOyPiguZLlSQ15ZjhHxFTgNuBRcBs4JqImN3WbBFwbv21FLij4TolSQ3q\n5cj/QmB3Zr6amYeAe4AlbW2WAL/OymbglIg4o+FaJUkN6SX8zwReb3n8Rr1uom0kSSNi6iB3FhFL\nqU4LAbwXEc8Pcv8f0XTg78MuogfW2azJUOdkqBGss2nnNfFDegn/vcDMlsdn1esm2obMXAusBYiI\nbZk5NqFqh8A6m2WdzZkMNYJ1Ni0itjXxc3o57bMVODcizo6IE4GrgfVtbdYD36zf9XMRcCAz9zVR\noCSpecc88s/MwxGxHHgYmAKsy8xdEbGs3r4G2AAsBnYDB4Fv9a9kSdLH1dM5/8zcQBXwrevWtCwn\ncOME9712gu2HxTqbZZ3NmQw1gnU2rZE6o8ptSVJJnN5BkgrU9/CfDFNDRMTMiPhzRPwlInZFxHc7\ntFkYEQciYkf9dfOg66zreC0inqtr+NBV/xHpz/Na+mlHRLwbESva2gylPyNiXUS81fo244g4NSIe\niYhX6u+f7PLcccdyn2v8UUS8WP9OH4iIU7o8d9zxMYA6V0XE3pbf6+Iuzx1IX45T570tNb4WETu6\nPHeQ/dkxh/o2PjOzb19UF4j3AOcAJwLPArPb2iwGNgIBXARs6WdNXeo8A7igXj4JeLlDnQuBBwdd\nW4daXwOmj7N96P3ZYQy8CXxmFPoTWABcADzfsu6HwMp6eSVwa5d/x7hjuc81XgpMrZdv7VRjL+Nj\nAHWuAr7Xw5gYSF92q7Nt+4+Bm0egPzvmUL/GZ7+P/CfF1BCZuS8zt9fL/wJeYPLeoTz0/mxzCbAn\nM/82xBr+LzOfAP7RtnoJcGe9fCdwRYen9jKW+1ZjZv4xMw/XDzdT3UszVF36shcD60sYv86ICOAq\n4Df92n+vxsmhvozPfof/pJsaIiI+C3wR2NJh87z6ZffGiPjcQAv7QAKPRsTTUd0x3W6k+pPqvpBu\n/7FGoT8BTs8P7kt5Ezi9Q5tR6tdvU7266+RY42MQvlP/Xtd1OUUxSn35JWB/Zr7SZftQ+rMth/oy\nPr3g2yIiPgH8DliRme+2bd4OzMrMOcBPgd8Pur7a/Mw8n2om1RsjYsGQ6jimqG4KvBz4bYfNo9Kf\nR8nqNfTIvgUuIm4CDgN3d2ky7PFxB9Wph/OBfVSnVEbZNYx/1D/w/hwvh5ocn/0O/8amhui3iDiB\nqsPvzsz727dn5ruZ+e96eQNwQkRMH3CZZObe+vtbwANUL/dajUR/1hYB2zNzf/uGUenP2v4jp8bq\n7291aDP0fo2I64GvAtfWIfAhPYyPvsrM/Zn5fmb+F/h5l/0PvS8BImIqcCVwb7c2g+7PLjnUl/HZ\n7/CfFFND1Of9fgm8kJk/6dLmU3U7IuJCqr57e3BVQkRMi4iTjixTXQRsnxxv6P3ZoutR1Sj0Z4v1\nwHX18nXAHzq06WUs901EXAZ8H7g8Mw92adPL+OirtutLX+uy/6H2ZYuvAC9m5hudNg66P8fJof6M\nzwFcwV5MddV6D3BTvW4ZsKxeDqoPi9kDPAeM9bumDjXOp3optRPYUX8tbqtzObCL6ir6ZmDeEOo8\np97/s3UtI9mfdR3TqML85JZ1Q+9Pqj9G+4D/UJ0XvQE4DfgT8ArwKHBq3fbTwIbxxvIAa9xNdU73\nyPhc015jt/Ex4DrvqsfdTqrwOWOYfdmtznr9r46Mx5a2w+zPbjnUl/HpHb6SVCAv+EpSgQx/SSqQ\n4S9JBTL8JalAhr8kFcjwl6QCGf6SVCDDX5IK9D8VLEat1FFTGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1194a8da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res2=np.array([0,0,1,1,0,1,0,1,0,1,1,1,1,0,1])\n",
    "[prev, pred, upper, C1, C0]=process(res2, l0, trans, guess, slip)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s_id</th>\n",
       "      <th>sub_time</th>\n",
       "      <th>prob_id</th>\n",
       "      <th>kc</th>\n",
       "      <th>correct</th>\n",
       "      <th>difficulty</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>342277</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:05</td>\n",
       "      <td>79370</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342278</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:06</td>\n",
       "      <td>79372</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342279</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79374</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342280</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:07</td>\n",
       "      <td>79377</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>342281</th>\n",
       "      <td>18709</td>\n",
       "      <td>2017/3/2 23:08</td>\n",
       "      <td>79380</td>\n",
       "      <td>egv_cc_9u11</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         s_id        sub_time  prob_id           kc  correct  difficulty\n",
       "342277  18709  2017/3/2 23:05    79370  egv_cc_9u11        1           2\n",
       "342278  18709  2017/3/2 23:06    79372  egv_cc_9u11        0           2\n",
       "342279  18709  2017/3/2 23:07    79374  egv_cc_9u11        0           2\n",
       "342280  18709  2017/3/2 23:07    79377  egv_cc_9u11        0           2\n",
       "342281  18709  2017/3/2 23:08    79380  egv_cc_9u11        0           2"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "final.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "stu_id=final[(final['kc']=='egv_cc_9u11')]['s_id'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5810 20\n",
      "[1 1 1 1 1 1 1 1 1 1 1 1 1 0 0 1 1 0 1 0]\n",
      "similarity stop at here 8 th problem\n",
      "stability stop at here 10 th problem 0.000640768447813\n",
      "++ condition is met\n",
      "18226 20\n",
      "[0 0 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0 0]\n",
      "18698 20\n",
      "[1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0]\n",
      "stability stop at here 9 th problem 0.011273151004\n",
      "similarity stop at here 10 th problem\n",
      "18699 20\n",
      "[1 1 1 1 1 1 1 1 1 1 0 0 1 0 1 1 1 1 0 1]\n",
      "similarity stop at here 8 th problem\n",
      "stability stop at here 10 th problem 0.000640768447813\n",
      "++ condition is met\n",
      "18700 28\n",
      "[1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0]\n",
      "stability stop at here 9 th problem 0.011273151004\n",
      "similarity stop at here 10 th problem\n",
      "18701 21\n",
      "[1 1 1 1 1 1 1 1 1 1 0 0 0 1 0 1 0 1 1 0 0]\n",
      "similarity stop at here 8 th problem\n",
      "stability stop at here 10 th problem 0.000640768447813\n",
      "++ condition is met\n",
      "18702 20\n",
      "[1 1 1 1 1 1 1 1 1 1 0 0 1 0 0 1 0 1 0 0]\n",
      "similarity stop at here 8 th problem\n",
      "stability stop at here 10 th problem 0.000640768447813\n",
      "++ condition is met\n",
      "18703 21\n",
      "[1 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0]\n",
      "18706 21\n",
      "[1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0]\n",
      "stability stop at here 9 th problem 0.011273151004\n",
      "similarity stop at here 10 th problem\n",
      "18709 21\n",
      "[1 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsXXd4VEX3fu+27KY3QgtJ6EV674QiEBAQRT9QfggiRbr0\nIhAEFFQUsVBF8ZMPREVQBJGQUKT3Jr2EEkggkEB6dvf9/TFbs7upG0DZ93nus3f3zj1zZnbuO2fO\nnDsjkYQLLrjgggvPFmRPWgEXXHDBBRceP1zk74ILLrjwDMJF/i644IILzyBc5O+CCy648AzCRf4u\nuOCCC88gXOTvggsuuPAMwkX+LrjgggvPIFzk74ILLrjwDCJP8pckaaUkSQmSJJ12cF2SJGmRJEmX\nJEk6KUlSfeer6YILLrjggjOhyEeabwF8AeA7B9cjAFQ2HE0ALDZ85orAwECGhYXlS0kXXHDBBRcE\njhw5co9kiaLKyZP8Se6SJCkslyQ9AHxHsU7EfkmSfCVJKk3ydm5yw8LCcPjw4QIp64ILeaJvX2DL\nFiAiAvj++ycjY98+YMcOIDwcaNoU0OvNB+n4u+X5wYNCTtOmQP364lpBj+PHgUOHgCZNgAYNAJms\n4MehQ8CePUDbtkDz5kWri2bNCn5/fmXkrFe9HtDpzOfffAP89hvQsycweDCgVIryFQTLlgE//wy8\n/LKQURzlyAckSYot9M2WcvKzto+B/DeRrGnn2iYA80j+Zfi+HcAkkrkye8OGDekifxeskBfppqQA\nFy4AV64AN28Ct24B8fHAgwdAUhJw6hSQnGxOr1IBnp6AVmsmAyNJGD8BM1kaz3NCJgMkSZxLkvkw\nfre8z5jXvxEKBaBWC+JUKkX95vaZmio6D71e1GHjxoCXl6gjy0Ons/3NeKSni//XCLVa1HlOki8s\nLDs6STJ/5vx/s7LEYYRGI3SxTAOY25bxMOpnLA8g6mfHjkJ3AJIkHSHZsFA3WyA/bh+nQZKkwQAG\nA0BISMjjzNqF4kaNGsD580DVqsDff9te12rF77t3A6dPCwKPiwPu3xek/uiRmUBXrxZHUZGVJeQX\nFUUlc41GdEKBgUBICFC9OlCmjH3ikcmAP/8Efv1V1IdMBvToAXTtat3x2DtI0QneuiXq78YNsw4N\nGgCjR9tax7kdUVHA1q1CriQJy79hQ1Gv2dnmT8tz42dmptDDWHd6vdAnJER0IsaOxGh95yRJo9xY\nO0auWi2uOaOjNdZdbp2AJInyWCIzU+Rt7LwKgqws4LvvimT9OwPOIP9bAMpZfA82/GYDkssALAOE\n5e+EvF140tiyBXjpJSAjQ3w/e9b88Fha1IWBXG5+IBUKs8Wp0YjDy0sc/v5AiRLAX3+JjsWIF14Q\nw31fX3G/EbduAX/8IazSs2fFKOL+fSAtzdq6AwBvb+H2qFlTWKE3bghCun4duH0buHsXuHdPkG5S\nknDXGC08I9LTxXH3rshv61bxuyQBbm5Cv7JlgeeeA1q2BN56S7gojHU3aBDg4QGcOwdcvix0iIsD\nEhJEno8eifrPma8ljhwBpk4VnXSrVsCLL4oy5YbmzYGdO0WdqFTAvHn5J6zkZOCjj4C5c82/lS8v\n/qfERKH7vXvi3JHeMpn4vy1RsqRwhbm7izoxHpbfc55v2gRERpplfP45MHSoaF+WVntuWLYMGDLE\n/H3xYrPrR6cTo5yUFPPx6JH195Urgb1785fXY4Iz3D5dAYwA0AVioncRycZ5yXS5ff4hyMwEFi4E\n/vtf4OpVQTIFtbaMrgB3d8DHBwgKEhZgjRpAo0aC8IYOtbb2X3+9cP72qlWBixcFodarJ4gyMVE8\ngNnZuXdGRuvZ0TU/P2H5jhgBdOtmP92kScCHH5q/T5wIzJ8v/O+bNgEHDgCXLgl3lVGnwkKSRL16\neIhOKiAAKFUKCA4W5PrLL+a0Pj6CoCyJViYTo5EqVQSpd+8uCN/SF+7IT00Cd+6Izsjece+erb6e\nnkBoqNAzMFB8Wp7n/M3XV9RX27bmDigmpnAW84QJwIYNwlCZP7/g9wNF8/nv2+eccsB5bp88yV+S\npDUAwgEEAogHMBOAEgBILpEkSYKIBuoMIA3AgLz8/YCL/J8qZGYCpUtb+1bzC5ks785AqRSkPGoU\n8OabwuKyh4JOtCYnA199Jazks2eFFewIkiT0cHcX5BIcLDqfpk2Bjh0FaQK25F2njsjn5k1bC9Xd\nXbhwXntNdAgqlVnG+vX2iSYpSbi/zpwxf54+LUYRuUGjEbq88YbwnVerJvLPDfbI6upV0Sns3Cnm\nSOLirF0akiQ6iooVRcd8/brouJ57DqhdWxD7pUvCbZeWZr5PJhMdesWK5kOrBd57T3yqVMD27YUj\nvIJOlGZkACdOiNHO4cPArl1Cb0C0gc2bgQ4dCq5HUeG8CV+nkD9IPpGjQYMGdOEJISODfPFFUqXK\nX9yIXE4GBpIREeTBg/ZlVq9OymTikyRv3ybffJMMCrKV5+dHvvoqeelS/nV+9Ij8+GOyRQtxv0yW\nt95BQQXLw4iJE8lKlcRnTh1mzybr1CE1Gtv8VCpxX/36ZEgI+frr5LJl5Jgx5PPPk2XKWKd3dycb\nNCD/7//IefPI334jL18W91mm8/W1Lm9YGDl9OvnwYcHLZg9375IrVpC9e5PlyztuF3I5WaMG2a2b\nKNPnn5ObN5Pnz5OZmfZl791Lvv+++CwOZGSINrl4MTlwIFm3LqlQmHUODCQrVyYlyfp/GjGCPHOm\neHRyhKVLyfBw8VkEADhMJ3Cwi/yfBSQnk+3akUpl/shekpyvw7p1ZMOGpJubdV4KhegwFi0iJ0wQ\n5Dl6NPnpp2SrVqS/v32iVyjIkiXJTp3IVasE+UycaJ0mJ3k7G1otuXYt2batIGhLgrFH8v36mUn+\nyhVSp3Ms+/XXRdlff118z8wkFywgq1Uz5yNJZJUqQmZ6euH0P3pU1HXPnmRAgFnnMmVItdq6HDKZ\nSPu4YeyMx40jjxwR5DlokOhkLdu0vz/ZsSM5dSr5889kbCyp14uOR6MRnZebG9m5s7mDCw8nf/yR\nzMoqHt1TU8lPPiFDQ63rcvHiQot0kb8LjpGQQDZqJBq7I4JXqciuXYXlVL269TWj9V6c+g0ZIsjb\nEWHmtDiDgsgOHYSF6sjKJB1b7Y8LwcG2+i9Y4Nw8UlPJyEiyQgXrDrtmTfKLL8jsbPv3ZWWR+/eT\n8+eL/97Hx3x/+fJk//7kypVi9KHX23amxsPH5/F0Ajod2bq1fR18fUV7mDxZkPfVq0JnR8g5AklI\nEJ1mWJiQV7o0OXMmeetW0fU+c0Z0TvbagvHw8hL/RSHgIn8XzLAkAUeHWi2syIwM+zJyum0eJzZu\ntB0RqNXkV1+RaWmPX5+iICdhGju30FDy2DHn55eYKEZMlkQjl4uRRufOZNmyZMuWwu3k4WFOU60a\nOXgwuXo1ef167uUxdqb375Mvv2weifn6kgsXOr9M69aRFSvaNwz8/MydkzOg1ZKbNgmXpiSJuuvV\ni4yOzn8eOh35v/+J0bWnp1lXmUyMzCZNIj/80LYDBci33hJutwLARf4uCPeII7L38BDX/yl43C6b\n4oQlYSYmWluvPXoUX4d26xY5bJjwc+dsDyVKCD/3jz+Sd+4ULZ/ERPKll5zbCezdK9w4lqNVuVyM\nDnOWZciQouXlCJcukePHC/eRcQT8+edkUpJt2jt3xKijalVrt6SHh3ADfv+96FgssXSpcEstXSrm\na8aNE+5Lf39yyRLb9A7gIv9nFRkZYojuiPRlsietYeHxpF02xYmoKEHAgBjlLFrkXPk6nZh8jYiw\n3y7KlXNufqToBHr2tO4EClKuy5fFiMRyglmSRBtYu9acztgu+vYlvb1FurAw8sYN55eJFJ3zt9+S\njRubCX3IELJLF3Gecy4kOFhMNp86VfC8Tp8W8w6AmBNzFFBhARf5P2uIjrYfhWHpt30c/noXCg+d\njpwyxWzdli9PnjhRNJkPHgj/e6VKQmapUmI+YPhw27ZiSajOREE6gaQk4X60dEEZ9f7oo9wnwUkx\n39OunbhHoSCXL3d+eSxx6JCZnC2PkiVF9FlKStHz0OuF26h0adH5DR5M3rvnMLmL/J8V5Az7M1qO\n0dHmNE/SX+9CwXH3rghZNf6fPXsWPFrn9Gly6FAziTZvTq5ZYz0ZbrSYu3c3dzgzZzq1KFaw1wm0\nby/8940bm90plpO2I0fmPoHvCEuXmkM627cvnIy8sH69mDOxN5KqVMn5+SUnk2PHiv/K31+ECdvp\nDF3k/29GRoZtTDgg4pUdTdi68M/D1q3m8Eq1mvzyy9zTZ2cLQmrb1mwEDBggwh/zwpEj5ncTXn7Z\nOfo7QmKieI/EHmlqNGJCtYCTnHYRG2sOofTxKXT0jA1++024yYw6t2wprHHLcnTs6Jy87OHUKfM8\nUePGYvRhARf5/xuxfr31CyrG4513nrRmLhQXdDphoRst84oVbX3Hd++SH3wgXhwDxOe8eQUn0Ph4\n4V4BxItqxWEtG7F/v+17JaGhzs9HpzMTsySJSdTCYvNm63j85s3JCxfM1ydOFP+PMbquOF1Oer2Y\nNDaGQw8dKjpVusj/34OcMfbGw929eEIDXXg6ER8vyMb4/9evL6y+Zs3MYbDt2gkDwVEcf36QmSlk\nA+Ldidu3nVcGUnRIb71lbsOWbbo4J/KjoswusKpVRX3mF9u2WYdLN2lCnj3rOH1Ghjk0dOXKouue\nG5KSxNvUcrkYJU6ZwrLATbrI/x8Oe66d2rWftFYuPEls2WJLmq1bCx+/M9G7N01umHxEmOQJrVa8\nternJ4hq3Djhw36cEVypqeYOVKkU7zDkhpgY80S5Mdomv/Wcni5cP5Ik3jAvbpw4IbgBYAOAdJH/\nPxgVK9oS/z85TNMF5+H5563bRXH5l2fNMre7NWsKL2f/fvFSGSAiY5zdURUUH39snnTu3t12pLR7\nt3j5yli/9eoVLuoqLU1MNkuScNEUN+bOJWUyF/n/Y3Hpki3pu8I0XbDE0qXW7WL8+OLLa90683zD\n9OkFu9fSxVO6tAhXdNabt0XFhQtCJ0C4SwYMEPH4lhFHdeqItY2KgtRUMQFf1A40PzCsUVQf0NNF\n/v8wvPyy9UO9fr0rTNMF+1i6VPj4AwNF5Mn9+8WXV0EjgRy5eJ426HT2Q6UDA53j6jIiJUW45uRy\n0ZkWJ/budfn8/1HIyLBeu8bH50lr5MI/BQcOiAiwV18tXqvaMhKodm3HkUAHDjxdLp68cPOmbQRd\nccToP3okQkLlcvKnn5wv3wLOivYp4Pb1LhQYn3wi9hw1bpgxenTum4644IIlGjcGZs8G1q0Dvv22\n+PIJChLbUzZoAJw8CZQrZ73BzL17YjvJpk3FBjD/+x8QHS02eXlacfCg2JAmJ156yfl5eXqKTWIa\nNwZ69xa7hj3tcEYPUpjjmbD8LaN55PKnc2jswtMPrVb4lT08xMYpxQ1jJJBaLTaaKVFCjFyfZhdP\nTqxeLXQOCxPvTUycaF4X6Pjx4ss3OVmEiiqV5K+/FksWcMX5P8U4eNB6mNmixZPWyIV/Om7cEJOV\nDRoU78tZRrz3Hm185QMGFH++RYVx/SRjiGxCgvlaYqLoyJo2zXsNoaIgKUnsp6FSkb//7nTxziJ/\nl9vH2WjbVgz9jNi9G/jrryenjwv/DgQHAytWiH1pp08v/vz69LHeyB0QbflpxqNHwqXzwQfAW28B\n27YBJUqYr/v7AwsWAPv3i/2Niws+PsDWrUDNmkDPnuL8aYQzepDCHP86yz852Xot8qCgJ62RC/9G\nDBki2te2bcWXx5kzIkwy5z7FT/NS21evkrVqici5zz5zPDmu14soKh8f57/dnBOJiWJPYTc38s8/\nnSYWLsv/KcLUqaK31+nE97lzgfj4J6uTC08O+/YJ63PfPufL/uQToFo1oF8/MQnrbBw/DrRpI+j+\n0CFg+HDxu0olyvQ0YvduMbF74wbwxx/AqFGAJNlPK0nA4sVAejrwzjvFq5e/PxAVBVStCnTvLibI\nnyY4owcpzPGvsPxzbsqsVLpW3XzWYblZuEZj3jPWmTh2TPiTu3d3bvjn/v1imeVy5awXNPvPf1io\nl8AeB1asEM9dlSoFmwyPjBRl+uOP4tPNiIQEsb+ySiVeiitim4BrwvcJIyfxu7k9aY1ceBowZ465\nTcjlYtPw4sCnn4o8vvrKOfJ27hT7z1aoQF67Zn0tNVUQrJvb02PcZGeLBc+My18U9CW4jAzRYVSo\n8Hj2id60ybwnsVpdpA7AWeTvcvsUFrGx1t+zsp6MHi48PTC6SiwRHl48eY0aBXTuDIwdC5w5UzRZ\n27YJWcHBwK5dQGio9XV3d2DkSPGuytChRcvLGUhKAl54AVi4ULw38/vvgJ9fwWS4uQFLlgBXrgBz\n5hSPnpY4edI8gZ6dDezYUfx55gVn9CCFOf7Rln/OHYkAseOPC882ZswQbaFvX7JNG3FenHH5d+6I\nwIJatQq+E5gRv/4q3BG1a+e+DLJOJ94zkMly3WKw2HH+vFiyWal0znr6/foJWcX9prIT3YFwuX2e\nECyXgLXsBAYOfNKaufAk8eWXNMXC6/WCSDWa4o+N//13ke/o0QW/d906sfRBo0amjUJyxaJFIq9O\nnQqelzPw559iTiIwULipnIGEBPEct2xZvLH/pCD89993+fz/keRv3DgaIEeNMv8+fLiwHq5ff3K6\n/RvgpIfjseOHH4Q/t1s36+WDR40S5JrTh+5sjB4t2mRBXihatUpY8S1bFuyN3ZIli39EkxN79oi6\nlcnExOnVq86V//XXokwrVjhXbjHBRf6PG2+/bSb+nJZPbKx4yEeOfDK6/Ruwd6/oQCWp+KJkigNR\nUULvFi3ExKglbtwQ14YNK14d0tOF2yYoSLiC8sKSJaIdt28vVqQsCDZsEPfWr184XQuKvXvNC7PJ\nZMXzfoNeT7ZqJVYpLcgOYE8ILvJ/nDAO6XNbc3/AADGLn5+HzwVbGMMJiztKxpk4fFhEyNSs6Tja\nZNAgESUTF1e8upw+Ldpf5865uy+MUUJduxZ+nsC4EcquXYW7vyCwXAa9ONvFmTOio/6//yse+U6E\ni/wfF6KizI0vIMBxuvPnhWUyadLj0+3fAmPcunH3Jbn86bf8L1wQ68SEhoplgx3h0iVRrqJsLJ5f\nfPWVqL9PP7V/3RiG2qtX0dYHOnRIyAkLK7yM/MC4nLVMVrzvTRgxbZoo1/btxZeHE+Ai/8eB27fN\nxJ+fOP7//If08irejTf+bbh3T5BI2bIiFrpZM/GgF/er90VBXJzQOTCQPHcu7/R9+4pImbt3i1cv\nvV68+KVSiQ7V8vepU0U7/r//K9oG8EY0aybk5bVPbmFx+7ZoE2FhYl/jxzEXlJYmtletUqXwo6LH\nABf5FzfS083En9+9dU+cEOnfe694dfu3IDtb+J3d3ISVRwqLGhBvYD6NePBA+Nc9PPK/G9SZM6JM\n775bvLqRooMpXZqsVk3MQej15pehBg92XkTLtWtifsbf3/mbzGRmiolojca6E3sc2LpV1NXMmY83\n3wLARf7FDcsY/oKgWzfxQDx6VDx62cPSpeItx6VLH1+ezsC4caJ+V660/r1LFxFV8rS8TWpEWppY\nJlipLPhCXS+/LBYTS0oqHt0sERUliLl7d7JxY5pCQZ1N0i++KGQ72w8/fLiQ+7//OVduftGnjxg9\n5WdU9wTgIv/ihNH3DBR8+Ldvn7jv44+LR7ecyLnZ9z+lA1i9Wug7fLjtNaP19d//Pn69HCE7W5Cd\nJBVuo+6jR4uHKB3Bcu9ahUKESzobyclCtkbjHFcSKQwB4PHMkTjC7duio27b9unZkN4Cj5X8AXQG\ncB7AJQCT7Vz3AfAbgBMAzgAYkJfMp5b8LZexLWw8cbt2Yj/Ux+E3LFvWmvw7diz+PIuKY8dEPbdq\nRWZl2V7X64XbomHDp+Ph0+vFglyAeNGpsIiIEPMEBQ2vLAxmzza3ieKMkhk2TOQxdGjRZR04ICzu\n9u2d15kUFosXi3KtWvVk9bCDx0b+AOQALgOoAEBlIPgaOdJMBTDfcF4CwH0AqtzkPpXkb3yBBSA3\nby68nO3b6dRFtxzBMhLpn2L5370rImSCg3MPizWG1z4NUT/GydJp04omZ88e5hqN40w8jtVFSUHS\nxnyK4tKynOB9kstHGKHTiR2/AgOfDn0s4Czyl4Qsx5AkqRmASJKdDN+nGNYE+sAizRQA5QAMBxAG\nYBuAKiT1juQ2bNiQhw8fzjXvx4ratYFTp8T5Rx8B48cXXhYJNG8uNsC+eBFQKp2joyXS0sSm26mp\nYnGrzz4Tewo85s3h5XJgnv4oGuGh6bdD8MZkWX3T9gYmaLVAp07Anj3mNdghNltadG83SkFrSpoA\nCZ2lF+HznwhgzZp86nEEDS30OAxvTJY1sNUjFxnv6w+iEVIgQQJBnEc2HmE7Jg5KApYudbxOvINy\n3IECowJb4e5dww9t2wIXLogFxdzcbGR4eADL0vagDMwLBcZBhcHuLZCamr9yVKsGzD2/EwEwP36J\nkGFa1TY4dy5/Mj78EGg4aQckmPmBkHB4fjgmTrSTeNIksWb9xo0AgC5dgDFbdkBpcX82JCyMCMfm\nzTnuz8oC2rcXu5Tt3QvUrVtwGQ5QrRrw2fkdUFnIyIKE0VXD866LkyeB+vWxtXR/SDf7FloPZ5QD\nAPaU3YPsuGwMwRCc53nHDTGfyA/59wLQmeRbhu//B6AJyREWabwA/AqgGgAvAP8h+Xtucp8q8u/R\nA/j1V3H+5pvA118XXeamTUC3bsC33wJvvFF0eTnRsiWwZw9+wVL4oQoAwg3xOIFADJd1KBLhHYIn\npsoa50vGR9JR1EIK1BZEkwEZTsETE1jfOvG4cWIzkm++Afr3N/08QTqOzkiC3CKpDsBeSJiu6ABc\nuwaULZurHvOkfaiLbBs9jkOJyWyWZzkePkzDxz670RwaGxkXcAsvb2oHvVasysh0PZgp0ugyCb3h\nfOWoe+iAJKulcvUAtsMXr050B7MAzfWjKLN+GuJbD0fycxFCXhZBAzHEfn0fAdDB8skmBHmX6+ML\nvdasm3h0xZ3U6g0SiJsbUlAKsJFxB0BoNx/AeM2QQIIESJL5Nwm49EsKyiDbRsZtKFH9JW8YbjF9\nBv8yFIrsRNzosgR6zwAkrEuE3I4OOgBBvfxhSTvqo1uhunoUaQ17IDu4hint2Q2PUNqBDlV7eImS\nE2ZZFLVBY0IAyb8nQWlHj2wAft38IJNJpvqw7NeNp+pTMbhzuTIIDSQLKQSghR4legaa8rLHpCRw\nc+N9eNnRIRkSGnxRwc5dtrgy9Qr4UOTwtJF/LwAtAIwFUBHC8q9D8mEOWYMBDAaAkJCQBrE5l0V+\nEpg4UVj6ANCqlVjS1hkggXr1gIwMseSuXJ73PfnF558Do0bhAN7BA7xoRVY66HAUfrbE6wAzpK02\nhJcJCUchR7LkD4mADIQMNPKC6VwGoDHuW1lVRmgBxEENJQgl9HCDFhpkQYIMeshNsmCSaQu9IS+a\nUlmDMD9wMrspxPWcw08px3mRnyIXXHiMcBb5K/KR5haES8eIYMNvlhgAYJ7BH3VJkqSrEKOAg5aJ\nSC4DsAwQln9hlXYaAgOBxERxXr6884gfEGbE1KnAf/4D/PIL0KuX1eUCuUssceUK8M470MsVSNV1\nsyJtAJBDjjp4hA+kk5CDkINQgFBAb0HExkOHtnCDlEOGG4hm0AJMKHTx5QA00EMHCZmQIw0K3IMG\nOkjQWzkTgCpIsStDAnAEPtBChmzIoIUELWTQQga9hRw9JHRHnENdfjQ0Xz1g6LaMnYax+9AD0ON1\n3HHYgXyN8ladDXNYgQQwFFcc3r9XqYJGr4daT5RgGsJwAzdQFg/gC2N3SgCVkOZQxkV4wsK2Ndxh\nLhENpayMLCsr1SyDOA8P5Gcbj2pIcajHNWigMLUtval9lcVxqHEXcWgBBdQO7083XJFDBw0yoIUM\naVBDCwk6i6MUtI5lyDMAyaIVSfbpRJOtcVgX6cr0PGrBKMPdoR4pikzxL1Bm+jfEuUxMwkFCIOn4\nfk83qOSEm0Qo5I4pMTvRfl0UBfmx/BUALgBoD0H6hwC8RvKMRZrFAOJJRkqSVBLAUQjL3+Emo0/c\n7RMcDNyy6MNCQ4V7wZnQ6YDnnhObYRw5YjWudOwu8cB9Nw9ImXooDQ+WCnqoDIRdA+ehghwpKAlV\nHtlnQIYMyJAJOTIhIQsSMgFkgciEDpnIRms4tpgX4S9kI9NwpCMbGchEKjKRhnQkIw3JWOA9CSUf\nlrS5P947Hr35AZDuA2i9IQLCfAAYzmXegMIHkPvgp3Q/BCDbRkYilOjjlgavzFQ8lKdBizRAlwog\nFUCa4RDnv2Mg3O2UIwt6dNIMg1zSQibTQakg5G7ZkPlnQRagg9ZPC623Fjp3HVatW4VSyaXslqWf\nfgqydW6gTg3o3AGdBoCn1bER3eFtM84A7sANfWDhepJloZx0HWWla9hf/jqguA7wOpB5HZuvzoDG\njoxMSOhccijgoQQ85ICnHPCQAA8CnpmARzrg8RDwSEL0nK0OCa9dkyVAvAcQr4KUkQ0yFUA6hCPE\nOFehRDQmOGwX7dAEgMbmWjnE4gKq4ie8jLIY5PD+9u41gWw3KLOV0EIJvYNxWzR25KJDG0CRAbg9\nADS3AY8rgOcZwPMYICUB2XogW4/ok3MdyogoeRoaZRjkKAttZhBSHvpCl6kGYD1Kz12PcDtXAEAH\nyNIA+UNEZ1/M1/0qlaChsDDzp/E8u5VZh8dm+ZPUSpI0AsBWiFpZSfKMJElDDdeXAJgN4FtJkk5B\n/IuTciP+pwK3cgxerl93fh5yOTB5MjBgALBlCx40C8fK5+/g3pEMtMUjG3eJGno0wiMg85HV71pI\nSIYCbkiCH27iMkJwDh5ogDS42XG5yJGEtugNHTLzVDEKUZDD1iWlhw6rkkchE5mYtnMaoi5H4XbK\nbWToMqzSLT+5HON/Gw91ttr0W4YiA2urbYWs0wn8tA5odRFo/7KEMyoZZPdl4AOCDwgkA/pHeuzG\naHRDDxs1ePKzAAAgAElEQVSf/278BG3WZ6gCwlsv4ZCPD9w9PODr64uAgACUKlUKoaGhqFy5IbYO\nvoYIVLDuTCU9/ltlM/wH3EVqdiqy9dnIdhyDgO+a/YBRUUOg1lqURZmB5e2XI7vONfioffBciefQ\nu2ZvDKw7EBfPXcShQ4dw9uxZXL58Gbs2xCACbWzKsQc/ARgAyasyJJ/yoDoEupRg4E4o1DcikJFR\n2pR+AeIxHudzuPKAk+UPAm/kmKHUQvR/KRB9YDyAVAl/e8WixqPQHD5q4kjIUSBinfm3ZADxMkj3\n3KF+5ItgVTCaVaiOoIAAbP5Yhs7Q25RFzE+6QwT+lYYkqwS54jlA8RzipMpYlvY6RvAbrMSbqGhw\n75l1AI7AG8z0gqc+Ax64iyTNQ2hldwH9TVAXaziuA7iFS5iASgixkXEWaVAoDgCoDl1aSTC1DHCv\nAYBXAAAymR4eHnqUKqXDWZxEdaTayPgbHlBn9ETqfQ2yszWG8thzzRJH4YX6eGQj4zC8IZPdgiSd\ng15/CORuAMcAJAFINw4okYC1CEJJm/vjkQB394aoVOl5lCrVFB4eNaDTBePOHQ1++w2Ijzen/xC+\naIgkp1r/eVr+xYUnavm/8ILY+s0SBbD8ZTIgkifRGvdNv+2CPyKl2tDrgb+W3sPmsfcgT9NBAx28\nkAkPECWQBS+LSBB7IIAYBCINmbiNUziNjTiNzaiHLOwBsB1AV0PagfgYvdDIiij00KIq5qOT90Ho\ny5ZFnTp10LdvX3To0AFudqJLhM9fDbVFJ5Ih12Jpq2+xIXx13pVxPAJdE73Q92RPBCUHIcEnAd82\n+x5b60UDbmJY7Z8OKAKD0KF8B8xsMxNVAqtYiZCkaeiIgRiDWKgBZABYiFD8ia9BzgWWLwcGDxZu\nuVatAAA6nQ6/XvgVa0+vxeG4w7hy/xran26Lt7a/ZdJjRfsV2F57u8gDEhQyBTQKDXzVvijlVQoV\nfSuiTqk6aBPaBo3KNEIj5Sm0qLMMLa62NsnYWCMGa708oWz5ObL11qMThUyB0p6l0aJcC4xsPBIt\nQv9AR/THaOkqNJQhXdLjM5bHn/gQ9eodxIWLF5CaYg7ZKSkBtVTA9uYa0C8YUIUBZ7pj0JXq6Jiq\nhj+ykQA3rEAYtvsnwqPxb2jQOBUtQkpDk6pFyu3buHThIs6dO4cLFy5AqzW2rW/wSZAedRPKm/+m\noKsYmwDUbPU/IKwS4vgQyYp46DxvAn5XALnhXq0bcL8icPJ5dP5rPEbiCjTQIwsSPkNlbPGJhtRh\nHBh3F4gjkAjRARmaoB9EXPgBANewDlVRwqTDedzF23gDaqQjC7ZzMUaoVCp4enri/v0W+AjT0cDC\nLXgEnpiAiQB2iLbl74+6dcNRrdpgKBTtcPq0HH+f0yHxngzZWWJE8SGOoyHMUXCH4YuJqAtJEgFX\nXl6Aj48W3t6p0GjuAriAhIQ/ER8fhYcP/wZALMZaVIV5hHsGaRiJYwCaAmgEMaIFlMpHKFXqOqpX\nf4hWrVR45ZUQ1KhxAF/pA1EFZjfTBWjwtnQdw4btwoEDB3D8+HHT/xcaGoomTZqgfv2WCAlpCQ+P\n53D7tgqe4w6gTGo6hj6uCd/iwhMj/4wMQGMYsnp4iJDJkJACuXzek06iMZKsSFcLCXFwgwpECWRa\n2RD3oUQiVEiBHClQIgMytMVdu8MuHXTogA5Wv6kh7IkgiKYWq1KhbNmyuHp1JAZCj96oAznk0EGH\nKOzBPEQCr7wCrFtnIz8zMxP9NvbDpkubkJadBhBof6q9LWnW2m4aiavkKgS6B6JlcEvMbDsTNUrU\nsJIpzZCAqwCiAHQAUB54FLYYp2a+jc+f98aP5dOg1Zs7PZkkQ1mvsuhetTtmtp6JkBJ3kJExHvD8\nE+gF4CcAKR2hVn+M9PRauHXnImLb1Mbm2u5Y0khCUkYSdLQzMWJ0vssA6AClXIlVL61Cn1p97P2N\n1rh7F6hRA+1eSMTN0h6Ym9kC09R7cV2ThYx3xWgnS5eFb459g/+d+h9OJZxCUkaSydMOAEjxA666\nAd7xQAiBw3JgSzsoqnZC8JAvEJsUC2opwm5uAhVPycBbelyxUEOhUkCrbQno1wFYDGAEINsI+NYB\n7hsm8QPOAmU3Qsr6DRX19/Bc9eqoWrWq6WjV6ijI9TASpEA4gBcAiBBmb29vtG7dGqNHj0bN+k0x\n/4ft+GnPcdy+LYPuXigQXxuIrwVADqiSAcqBl/oCVTYhLK03gj3Ko1JAGGoGl0fjyuVR2kPCH5s3\nYePGjWi3axemZGWhLdZjBxYZ9AgHsA5Ab/gjGu4qFdzKlUOFChVQr149dOrUCW3atIHcIjBCozmC\njIzJgGeURbvoAJUqEuPG/Y6Nv/6GC+cvQKu13D+7JICWAPoBiIA84BYgvwVdgjuAegD+AvAcNCFx\n6N47EW90UuLEwZ3Ys2cP/v77b8TFxSEjw3p0KzzeawC8mqMsb2DDhiHo2DECly+rcOAAsH+/OM6c\nMUchKRRZ0GoBVPgKaP9f4KQvcOBnhITcQ2xsJQBAeno6jh07hv379+PAgQPYv38/rhu8EUqlEhUq\nVMCVK6HIzv4eQF2Qt1zkX2AolSLeXCZDvuMhDYj54h7Wj7yPF3DbrrtFCwln4YVUKJAOCZlyBWr2\n8UDfZSXEhHKtWvi6d29MmDABrz6YjRdRy8bnvx7HsBxjIJPJ4O3tjcaNG+On+Hh4nTgh4vkXLjSl\nd3ffgPT0LwDP7RYPR3uMRyt8pJ4HpKXh2+OrMC16Gu6k3oE+F5dHTkxvPR3vtX2vQPVjwtGjQIsW\nQJMmYnNwpRK3km9h1q5Z2HxxM26n3LbSRZ4ZCO4fBH3YZ0BIGhCnhuzyGCgbr0am+oaVaImAJJPB\n280bFf0qIjw0HAPrD0T1EtXhP88fDzIfmNL6qf1wf9J95Auvvw78+KPQvWbNfBf16oOrmLf9S/x+\n+BRuqbfZn3gk0KdWH1QNqIqqgVVRLbAaKvtXhoekAqpUQZy3N5b06IHo6Gjs2eMB4HvkJBq5fDja\n9PHA2QeVkXCuKXRXWwF6JeB5Bz61dqNLtyx88nZXlPL1RcuWG7Fnz0LAc4dFuwhH8+ZjMGxYCpYt\nW4b9Jy4jy70uoGoKZDUB7jcGMkUYKNRJ8KpwFo/8o4C/awP3exj+qAzIa27AqIGBWDCiPa5cuWIi\nqgMHDuDYsWPIzs6GD4CT8EU8dGiCRxZPSjjUeAHp7bcAf/wBKHL3OsfEAL163cX9LqWBCjrgshKy\nddfgWyEZD+95QJsYYkh5EpB/Dkm2DdTeBAyGgUwmg4eHBx49agxB3osBvA2gN4B9ADJhOf6QyWQI\nCAhAxYoV0ahRI3To0AEdO3aEu/tyB51pEwDzERAQgN69e6Nfv35o1KgRJEnCw4fA5h0JWPbbCcTs\nTgNiWwEZ/iI/zX3g1VeA8jvAmY759/bt26b6Xbr0ApKSlpraBUkX+RcIw4YBixeL8wcPAF/fXJMf\n+vkBVvW6B39koTQyUB6pUEPEUzuawNFtC0f79ua53V9++QVDhw5FaEICdBAz4QITMRBBVlb7WpzA\n11CCHGUW+tlnwJgxQMWK4oUxi0njTz4Bxo/Xg+EvAK22ALsjgJ0b8EalPvj2wnq06wfE2AkjVslV\naFqmKVb3Wo1g72BIs+xMDubSKHPF3btAw4bC7Dl8WLyIZgenE05j9s7ZiL68C/cy7jiMt3STuyHI\nIwgtNVUROX07Krw5Dor5H9lNq56jhrvSHZNbTsa8v+YhLTvNZLXnCuM7GZGRwMyZeSZ/+FC8oxYT\nI45jx0RxVQG3kB0xGAyNBpQZMDYU2Z36GNHgHSwc2Nf2HbElS4C33wa2bwfatYO7+2Kkp69DTqKR\ny9vh8uV+CA0NBQCcuBaL8V9GY09UINL/bgNkeQPKFKir7US1KjJcieqAh006Ak13AHueh8fBTajf\n6Syu30nFrbPBZuKUtEDAScDjAMD9QMpBVA2S4Ofrg/1H1EC25QhkNyDrAGg9gHJ/Ab5rgdNL4a5R\noWHDhmjatCmaNGmCJk2aYG5YGL7SavEfCBvZiBAAsffuAQEBudbxmZvXUHNFeYcPWvDWPahaMxPN\nG7gjomVZNKlR1hSzv2vXLqxcuRLbt2/HzZtVAKyFrdXeByXLXEQGg5Gc+DyQ1QeSJgg12p7GO0MC\nMOCF6iZ5o0ePxqJFi2zUGDFiBCIiIvDdd99h48aNyMjIQGBIINT11UislIh0T4toIkrAlk+Bg6OB\n1u8B7WYiPDQcCzotQP3SeYdl16+/FseOLYWxXTiD/Iv8inBhj8e+vIPFEs2VcJ6zcIIxiDEds3CC\nCmg5Qn6R03CaX+AIf8cu0/VV2M85OMHROMftFvdZHlGIIZBKleoLAmFGJ4TpaAGwD8CKFSvaXLM8\nTLh8Wbw2r1DY3TAEM0B0GUooUokqGwj3BOKNcLpPBR+qwF+qgrJZMpZdUJbfH//eYdUgEkQk2GJF\nC9N5oZCVRYaHix2lDh92mOz6dbF0SpcuIik844je3YjpSpH/u26U+nXg5xtyLEbWq5fYai/ndolF\nQXKyWFagZk3On5vN6Gjry9HRYpmcrVvJyZPJJk3EXwKIZWjatBGrT+/cSf7xB4ke/YmZIKZBfA5s\nTEwIJCJBxeDmHL18tfVyRenpYgnmdu1IkmFhtu3GeCiVSg4ZMoSxsbFWOiYkJbPfR6vp12oN4X3D\n0My1hCyT8LpBQG9s+pT53GKZxnvZZVgMF/1wjHfui9VnL126xKFDh7Js2bKG/HoSSCAQbvgeLr7L\n+1PWcB7he0XI9I5lYIeVjD5yzUqn0cOG8TjASwCVBv0lgOP79HH4VyyL+Y3lprWjNNlP1F2koQ6N\n54Zj7s65du/X6/U8fPgwIyMjaQglJ7DQogwwlUWSzGs0pWdm872vDzC01S5CmUqAVJa4ynYDdnDX\nsZucP59cs+YG/av6EwPAur7gD+PWc+bch/x478ds+XVLes7wJLqDCDXn41PVh69OfZWx8bEs93Y/\nQpFAyGcRigT6vPkifef5EpFgjzU9eDTuaK7NtG/fvjk5ovjX9imu47GTv3HBNkniLJzgFuy0Ie7N\n2GH6/j328X2c4Ds4y9n1r1qJmoAtNvdvwU4OwVcEZIY/SE7gOQKhDA4O5u7duwVLAOTJk6xQoYLd\nB7xChQoiE51OrH8DiA2mLdD+3flErf8S3tcMD7Xh4fa9QoyoxJpf1KSuQnlx7+PaFGXpUrO+331n\ndUmnI/fvF0vj1Klj6oNZoYJYaTgqivScVI2YISOmK8RnjzcJiL1Hbt0yCNq1S9y4bJnz9B46VKzi\neuAAo6PFUi5btojlmfr2Ff2ukewVCrJ5c1GOqCixwrMlhgy5TPQJJF4AURLis08gX3vzGGtOGURM\nDDB0Ai34zoo15k5gwQKRwd69XLRoESVJsmoTkiRx1qxZfPvtt6lUKqlUKjl06FBev37dKv8zCWc4\ndds0egxrQ7R+j/C8Jeo6eA8V/3mVX2zbQH0uC+VptVpGR0fzjTfeIDApV9JMTk1nk8HfEGE7RB6K\nNKrqruOkr2JM8hLGjiUB/gpwMMCbORbFS8vMZL+v36PXxFrEu25mgp+uoGZiRXb5fBTdI0uYOwCL\nTqD+kvq89uAaU1NT+euvv3LQoEEsU6aMqb6aNWvGuXPnUqPROOxMv/zyS2bnWEDuRkISB8zcSd/q\nhwnoCJAewZfo5p5F9xkliZmg93gl5f1bEDNlJn085nqw6fKmfH/X+zx0+hBnz57NypUrEwAViudF\nxylrK/KWtyWQwPc/2sf3drxn6gReXPsij922v3/Bzp07GRAQYCoPXeSfT3z0kZlxrl7lNgeW+1bs\n4Dj8zanlL+cqDviMA/Ext2EboxHNbdjGgfiYwDQCYIkSZfnWW4dYpozIsk0bMiaGZGKi2PPVYP1E\nR0dbNcZoS7PzzTfFzc8/T5Ls++mXRL2vCb8LoiiSlgjeQ9RfTGjuEpV+F41VlsnXXye1n30h7n/z\nzeKpUwMiIsglvbaZ6xfgkl7b+Pzz5E8/kf37i33FAUGirVuTH35Inj1rXrAzOppEn65U9g+juoKa\nyv5hRJ+ufO01YV17eIhFKdPT9GS9euRzzzlntc8dO4RiY8dSryf/+kssimpRFFarJnbm/OOPvLdo\n6NOnj12S6du3L0nyemI8n5sykJjob+gEWnLc1z9Q/yhFbBHatStJ8u7du2zevDklSWKLFi1412IH\nsNjYWA4dOpRKpZIqlYp9B/bl5J8ns/bi2kSkGOm1X9Webm92FiPB1rNMI8KJf0600Vmv13P//v0c\nM2YMS5cuTQD08PCgXC63WxY3OzvavfPlFirrrREjUJBS6C76VjvKT3tsoR6gHqDO0C5atL/PRu/3\npWJiMDFTMlv3Uz0YNLUJI39dTq1WS9LQLsaVomq8H9UN1FSN9yMmBLLM3Eqm+2QvywgZ6OXlxV69\nevHbb79lQkKCSbdRo0bZLUdwcDABsFatWtbPnQX2nLrO9oO32Yw8TMdMcEb0DF5Pum73fr1ez337\n9jEw8EO7HWm9emJp8AfpDzhrxyz6fOBDRII91/bk8dvHbeSlpqZy6tSpBKCli/zzCYAp8OIs/MWv\ncNgu8RuP3DBv3jwbq8yhy4ZiRL9okRjVA8IjsqP3YmFpXrzoOKM//yQBnvKoSlnDL4kSp82EVPoQ\n0fQTdv90mjAY3ZKIXr2Ey6bjaELKIkD6+en5t+I5sa1kMWLBAtIDjxiFcKZCwyiE0x0ppi0RfH1F\nX7d6tej77GHIkMv09u5Bd3d3AqBGo6G3dw8OGXKZly+TPXsKWeXLk+tHbKceKPo+q2lpZKVKvBda\nn5/Oz2CNGiIPT0/RvwDkRFuutIvz589zwoQJdHNzs9sm+uRwdVy7d5s1prxJTPITncCg1pzVb6wo\n19Hch/8kGfcwjjN/mcmgNkFi3Q05WLJtSb7363u8/ei2IE33BHoO6sb5f82nemCEqQMI+TSE5+6e\n46lTpzh16lTTCNTNzY09e/bkunXrmJqa6pA0AbB9+/ZMteN6++PQBQZ2XEb4iBGpBC0HYTET4cdt\nCKdGkUD0CzcRp3xSSdac3ZMxZ4/YLWfOdqFQKCiTtScwgXgOlKaKzsPtPTd+tvczh/UVGxtrcmcF\nBwczNjaWer2eP//8s8nN9tJLL/HKlSvm/+jBNb6y7hWq56ityN74GTxa4l8L3snzvyJtXTY5jQIj\nHqQ/YGRMpKkTeOmHl3jizgkbeXBt5pI/fIf5/BJR/AW7GYMYrsMe/mHh3smL/NPT01mqVKlcCd/y\nsGxARqSlkZ99Jpb4B8i2shg2KXGJCxaQU/6cQkSC06Onc8ECsk6DG5yjHMWm0i4z4QedIBp+ycbv\njrGSGxFBRkYmsVmzZpQkic2bN2dkZLKps1Egiwsxitr1G4urepmZSY5QLqYCWZRBS088ZHPPYxw/\nXhjW9pbrz4nevXvn+XBERZE1a4pytVPu5Mm2owqts14vOuHX8D3dlFoCYsOrFSvI338Xrp/p08Wn\nA6OQ6enpXL16NcPDhUUnl8tZrlw5u+Xw9/fn5cu2o8mr9+JYY8oAYpIY9vu81pyd633K7dvJY3HH\n6POBD0/cOcHoaHLmnFQuP7Kc7Va1o2yWcDfUWVyHk36cxD5v9KFCoaBKpeKIESM4bVoyf/89jVOm\nTKGvry+nTp3KX35NYkiXL4h2IErApHOnTp34zTffMMnOcsw5STM6OtpURo1Gw59++slu3SQmpxKv\nvkh3/6MESBXS6Y0kVo8IJ2aCPRePZ+Kj5Dz/J0ftol69evz777+p1Wo5cvNIymfJiUgw5NMQ7r+x\nP0+5lkhLS+OcOXPo7u5OpZuSEe9GsMLCCibCd5/jztd+eo3ukYHW7qd3lfy+zUv5yiO/5G/Eg/QH\nnBkzk94feBOR4Ms/vGzqBOIexhGBeEQX+dtH9Od3ORZnuRiHTP78hTjKUTjHywdS7fr8t2AnZ8Hc\ny86aNcuulV+3bl3T8Nje4evry19++cWuXmlp5MKFZCn3JAOx64luA4hJPkT9JYSUTQmCjDy8zhH1\nVrDSyGFMT7e/naEj//CiRYu4Zw8Z5JNOgKwuPyfcTk5EQgLZuzfpIU8jQLrjkamzGv1/ifnaiyM+\nPp4zZsxwaDHnfDiys8Uy//6aVMqg5bC+SQVaaj0hQXgAq4SIevFRpXL4cPK4YYRt9PkbCT/nd5I8\nc+YMx4wZQ39/fwJg+fLl+f777zMuLs7GL6vRaOjl5UVPT096e3vzxx9/tKvX1bu3WH3KG5QMnQBe\n60rvsQ2JSDDg3dpUeSVR3r8DEQlWXlSZM6Jn8O+Ev61lXL3Kt956iwqFgkqlkmq12qSHUqm0cuPI\nQmVEF7DMrDI2cvKD8ePHm9pdu3btrEYB6dnpXHZ4GYM/CSYiwaAqSwztQkePThMYdTnvEVtcXByn\nTp1KlUqVr3YR/yierVe2NhF222/bMjHVwTDTDi4lXmLnbzpTNkNmsuzLzC3D746LuSuj+8kzshTn\n/zWfbu+WEPNS77rxlRkfU6fL3QVpr10EBARw586dud53P+0+p0dPp9f7XkQk2GtdL7667lWiNMhn\nkfwBsj8uWRF3f1wiQI50v8DZOMmNBiv/B+zhEvzG9ZhgJUOSyOk4xO3YzmhEczu2czrEBE/JkiVt\nGptSqeTmHJu7bN682SrN5s2befnyZTZo0IAA+M477zAzM9NuGdLOX6dimpzo+TohTyekbAKkm/oW\np2Aul5UI54PUvDfGcBQZEhYWRpLUasl57jPpgwcE9Hz55aJvkHTypNhbW5LEf1EfhzkVcyiTdGzX\n+CE1EH7fTp0c7+1x9uxZDho0yET65ggTWwvP3gRl4qlbHCF9QbmkpZ+fcK117ixcUJZYsECMjrZt\nI199VWy9C5At3I9ylc9Ipt56YJV+/nzaWMybN6dz9uxMrlq1ii1atDC1h1deeYXbtm2jLseG6Ea/\nrK+vL6dNm8bU1FRevXqVjRs3JgCOGDGCGQ72Jr5y9gRl0yX7/uVI8EjckVwnbEnyypUrDqPJ6tev\nz+vXrzM1M5XPf/c8EQlKkRLHbh2bq0x7OH/+PENDQ01ktvy75YyMiWSJD0uICdml9an8v+6EewLL\n1fyAKmQQIAcNEqNFezh9+jQHDBhAlUpFSZIcjqQcWcy7Y3ebOh35LDnHbh3LefN0jI4m/7z0J+Wz\n5Nx+ZTujo8l583RctH8Ryy8sbzVpG7E0grWb1yYANm/enIcOHbLbLpZ/dpQefUV0XPDoPoy98zDX\n+rLXLvKLxLREKmYpzG3hWSX//rhEb2TyExxjDGL4CY7RB5mcitOMQQy3YQc/xVG+jz+YBUOYRg7k\nZrlbHo0bNy6wfhkZGRw5cqTp/qt2toIcvfozlqz0tSB9g49eXeNrPlCBGUqZRXiLYyxbtowymcyu\n3kbyJ0nOmcM4lGJ19WUCpLe3IMOCYsMGskoVmqz7UqXIH5p9wmiE0w3pJuLdWHG0weevZ7Vq5qkN\nvV7PmJgYdu3alQCoVqs5ePBgnj171q5lZLT6+vfvb58se/fmKc+mbB8uOs6SJUWHZNTDGFjl72/+\nHDOGPDNqifhhwwYbkUY9jD5mtVpNtVpNDw8PAmDlypX54YcfMj4+vsD1l5mZybFjx5pI+NKlS3bT\nXRzZly/1kogZwpWBGTKWnV/Bru/XEfLrZlj/93q6z3UXBPZJME/Hny5wud4a+ZZxQVIiFOz0dSfG\nXI3hxx/rCc09qt94iR/u/IDr1O2pkoRhEB5u3hxLr9czKiqKnTt3Nv33w4cP58WLFwttMX+671OT\nr95j0Av08sug56Buwo0zsCtVXklU9H/eRKY1v6rJtafWmu7X6XRcuXIlS5YsSUmS2KVLF/r5+VnN\nSQUEBDC6Vi126dSRmCGj4p3K/O8220laZyHuYRxf/uFlKt5TPLvkbyR8L2TxFVynj6EjiEIM38Vp\nRta4LPaINbLUN9/YyMiN8FUqFaOiogqlmyV+/PFHent709fXlxs3Cp/7+DVfEPWWE7IsQp5OdYX1\nhOYe0XI2fWQJ/EMKt6uvJb755hsGBgbmWoaQkBDzDZmZpFxOnZ8/Bw40V0uPHo4tMCO0WuEmCQw0\n31ejhoh84bVrpCRxknw+F3yoNd+0eDGjEc42Ne/R35/099dz6tRtrF+/PgEwMDCQkZGRVhEZpK1l\nlJKSwpkzZxIAW7VqZZOe+/aRAPVffMkNG0TYqFFHX1/zeXi4mGxOT6cIMVKpxDDADhyRZlhYGGNi\nYvK0uvODjRs30s/Pj97e3ly3bp1tglu32PWFMsKtMEMu/MsTA3jwTP47nIL4mNOz09nxu46mUcCY\nLWPyLKder+fOazvZfU13IhJUjlbSM8jT1GGuWbOGERE5RmIjR3KbvCPr1syiSkVWqqTn/PkbWbdu\nXQJgyZIlOWfOHN7L4ccrrMWcnp3O3j/2FgTfL5xwe0A0/dg08Y1IcODGgYx/5Lhek5OTOWHCBIdB\nHn2bNCEBLv5qFeUTygg30LylebqBCouhvw0V8z2loeezSP7r8Rd74CaNse0v4gZjEMNoy8lao0/C\n29uujNyI05m4dOmSID11CFF5qYn00fgzVnvlO8Itib2e68EzIe6MRjjVikQb14UR3333HYOCgmj0\n63fu3JmzZ8+22zDd3NysRxxt24r62LGDhw6ZQy+9vMQe6TnzfP99slw5wwtYENXZrh157pxForp1\nxcVVqxgfH2+adG7TqBFTZDJmjB3LyZOXU6G4QCCLQUHvcunSpUzLGRyfB9asWUM3NzeWL1+ep09b\nWKZ6PdmokYjF1Om4dav1XvZBQeT58xaCdDoRpO/vb3fvYL1ezzZt2hTIzVBYXLt2jU2aNCEADh8+\nnOnp6aZr0dGkondXdutahttH92DJd2oSM2SUja7CLfuu5Ut+YSzmDWc30GOuBxEJll1QlqfiT5G0\ndkw2f+oAACAASURBVJdk67K59tRaNlxmmI+YH8AZ0TN455GozylTpphGo61bt+bly5dN7aJzzZpM\nBZi6dCmHDfueMlkCgQcsV24gV6xYYVUHzsRbX64gyv1lahdo8indZrtx+5X8R4t1797dfrt49VXS\nzY0cNYrnb8azxFjRiZYd1YfXbufuBioMeq7tyWGbhhFKnOGzRP6/zk1gO9yhB7IpQU8ltFRBSwl6\nTsHf5kidZs3MDJADRnfM4yL/9zZ8QzT4SrxpKc8gqnzOVnN6kzTEx/eJMesKcMnbxxkRYS1j9erV\npnkISZL4/PPP85aFWyhnTPigQYNMFtiJEwZXwd69Io+WLUkK3hwyxNxHAsLfffWq4FLjbyoV2a8f\nef9+joKtWycSVK9ud9LZG+Aaw3mLFl1Yv348AXLUqMLNOezfv5+lSpWit7e39dzL99/zLKqye9N4\nK4vfzU18jrEMjvr8c/FjjhfQSPLQoUMOib84yJ8UbqBx48bROLdx0eAfmz+fjI7cae51NRpOmbaW\nmOxLaVxZfrflTL7kF8ZiTs9OZ+f/djaNAkZtHkW/D0Q4qmaOhqGfhpomnRcfWszULFuZFy9eZPny\n5e3Wox/A+Ya20qxZb4aFJVMu1/PzzwtWd/nBvpN3WL7FAdGW1fcIZYphfk1LTe/+BZKV60iqVy+y\nRAkyK4tanY5d5801uIGq8L9/5t9VVxDgWQn1vHcvlV1wkyUgIjQq4iE9kcVPcIwrcJAeyKIEPXsi\nVrzNamSu6dNNMjZs2JBnfD4Ali5dOt9/QG6Y++sqotEXgvBlmUTDr1hjUCd6eXnRz8+Pv/32m0g4\nZIgV+XPoUJOMH374wWpuol27djZvdDrMf+5cAiIuOsYY5hMYKN4vsLC8jx0zh59aHmo1OWuWA6LW\nakkfH0FMFy44nHTuC/DMqlUkhZwxY4TsTp3IBw/syM0D169fZ926dSmTybhw4ULeuaPnsCFaypFN\nL0Uqu3Qx+/yvXhWrQADiDWJeuybeFOvc2erlsNjYWNODHRgYyDFjxhTKx1wUGN1AXl5e/OGHH8SP\n779v/jPkcvL997nl6AnKJ5UiJvnz47UFC2cssE7nNjqcdEYkqNPr8pTh4+Njt130AXjc0CYfPiS7\ndRPFHDYsf2HBeeHGnRQ2e3WPePaUqaze/iDhfpeeg7qx58cfEMpHhKTl4pX5b4Q5R1JG93B0dDS5\ncaMowKZNpvSLN++gfGJpYpqar3ywzOluoGeC/F9GLMsbQghDkMIXcZ2AdbTP1zhIN0N45HlUMZt+\nJB88eECFQmHV+CIMprW9aJ38Yv58MTwfuWkkEQm+88c7jI4mXxh4nGi8yED6WUSDJWy54HXTfRcv\nXjT5OMePH8+7LVpwCkBfgJMAJr7xBn/66SfTa+oAGB4ebnfSOC8sX76ckiRRJpOJeOwJE0TdzJhh\nlU6vJ0eMMI8CqlbNQ/CwYSLha6+RpMOIjNcAcvz4HDqJJRIsJ4ILgpSUFHbv/h8Ck6lUplEu13N4\nowNMQCAjWqdYua9iY4WHRy7Xc3+TUeLtrWvXSApf7pQpU6hWq+nm5sbJkyeb4tyLEpVhRNzDOLb+\npjVvP8rf0hrXrl1j06ZNCYBvv/02723ezCkyGX0BTpXJmGqINT1w8RLdJlQgpnpwwpI/C6xXfqHT\n6zh6y2gb0veY65Fvd0luRgFXrjSl02rFy3QA2b69nVFmPpGWrmXvcfsouScS0DEsPJp/nbpqelaN\nWLLhCKF8RJn7Ax48neBYYA5Ytou2bcUyDb1792Z2aqp4O7t3b6v0wg0kJpW9+7/ONT8/smoX0dGC\nRwqDfzX59/e8ytq4T4D0RwYjcIsX9jp+CE+eJANVSSyNWzyPymR6usk/bjyCg4PzVbH5gTH+2zhx\nhFdeIhT/T953h0VxfWGf2U5vIiAI2LCAGntvUWOLBUtsEU1i11hiF5UFFHuLLdhjiy3GXmOPGnvv\nGmMDbAgoIGXn/f64O8P2gvj7vvi9z7OPArOzM7P3nnvuOe95Twbz8iXZoMpLUWNGZ5PvzczMZBot\nWqOv0l4fZzBJ6tWrZ7JgzB5s374dEokEHMdhydy5zPP39TU6bvZsZvzr1dNnzBjh6VN2DkdH8JmZ\nGD58uNldVAuVCggMNJJhOHoU2kSw+QIqU9BogLVrgaJFBZGyP1CjRgTe3L7NOJw//mj0nidPgOLe\naXClFJwZsQU5OTlYvHgxvL29QUTo3r07/tUuCAWJAbsHQBItwYDdA2x+T3Z2NkaOHAkiVnyl0rKd\nHCQSvd3HvYQEOI+sAJooR8Q003UDH4PLiZdRe0VtkS6pa/zdprrZfB5zxr+jTMa2fwZYtYp9jSEh\nBrkaK+B5YOKiy1B4/wsiwLXsOazaZ1ofR8AvOy6AVMmQez6zawHQxfTp0/MWgP792XY5Vb9wjYWB\nJrPk/eDSqBTXCZJoCdrED7BYQGgNn6Xxn9LoCWrTK8hIAyXlogElYdUAG7ynzExcp1B40ws4SRJB\nFCIONqVSibf5iTNYAKmJGX6HVyD/M2DJ51xQpeWoMrWTTeeorqUPmkrW3s+PW2wGJ06cgFwuBxFh\niZARvZhXTi8YfsHgG/6sh6pVASL8OXQovLy8xNCSqftoUKIEkys4fdroNA8eAGXLsl1AfLz1ezh6\nFKhcmV16lSqscnjNmjVQKBQoVaoU7rRpwzx7g8mHpCQ8dQtFCdVTODhkIzCQVYzWr18f58+ft/0h\n2gg9KQCdl2qyyuZzCBXDlvIOSSlv4TWqDiiKQ7Px8QUic5SSmYIhe4dAEi2B9wxvrLq8CspYJTym\neaDa0mrivWRk25awN5ULIiL0KFWKhbJ09IoEnDzJnCp3d1bRbQ0b9j2AR8h1EAFy37sYu/iEzSGW\npbsughzeQOb5DGev528BmDFjBogInZs0QY7BjkYXsmjFR48LXXxWxv/IsldoTAlw1sbvq9FrDA22\nwyOTyfCSCBWpHIiSQPQcRKWZkuYnwOBdP4I6dWDxQwLI5zJoSHEM32+b1gdevsQ3ZnIQehz9AsL1\n69fFeOVyIqZepoURJQ95xVF62LEDqUToqlKJ19q+fXtkZmYaJZ2FHcFPUin4IaZlGFJSWAieyHwi\n+NatvJhwYCCwbh3bAQj466+/4O3tDXcXFxwiYhoauujUCZdkMtSp3BpEd8Fx7xAXd7xAKJum8DTl\nKarEVxEntyxGhu6/d7c5/APYTtNMy0xH4NiWIDWh6rA4ZGfn7554nsfaq2vhM9MHnJrDgN0DkJxh\nHHvpuKkjSE0oNq8YcjS2Ze11x0Xt2rXRunVrEBGWEZld9f/5h+n2SaVM9tsUzt14iRL1WDKXc36B\njqMP4r2ZKnhLWLZbuwB4PMff1z5yAXB2Rk7DhiaPSUhLwNdr24sqoJKJDnaPC138541/CIXgMB1F\nE3oOZ2KFTmUoFV2l1o3+xo0b8zxlYvFyJ3GilIWT0zv4+jJad0EjZsdyUJltzOhLspksg5Y7bCv+\n7tABbmbCJZ/C+APA06dPxSRcLMfZlV3jc3OxUqmEr841XrlivqCF53mRWRXt7MwCuyagmwiuWhW4\n/SQR9VfVx/V/XqBNG7YDcXUFpk0zlk8W8OjRI4SFhUFKhGleXqhVsyY4jkOV4sXRmRhDysvLC7Gx\nK1CqFA8nJ6YMXdC4+fImai2vJRp+IWRSe3ltu85jD0c/KycbYZO6g9SEEgOHIz3DeiJWFzde3ECD\nVQ1AakL1ZdVx4bn5HgwA0ORXJjERtijMqLLZFmRlZaF58+bgiLAxNNTscamprNcDERP1e5LMYuWX\n/3mE0EbXWXGkLAPVuxzAvy+syzi8f/9erzpXN4+zfPdFkMNryDye4/QV+4v3AGDmzJkgInxDhBwz\n+bnW8f1BURJwExWgSRJUnmh7SNAQ/3njH0hlUVybzC1EmWhLT/D6tfXkmqCpYhQuIRYnB4CbN1nF\nZ0EuADwPKNoNYMUikg/M6+/RGBUWV2AhIMeXiFp5zOI53r9/j+E9e4IjgqcJ3RJBl+dTITk5GWHa\nHUCb4sVt8oCPHTuGMtr3FJZIMHfuXJs+S6PRoJeWPjl30CCLx2pl3+FU6A2oQTSkCiYF0K4d0+Ox\nhrS0NIRqJXoNX182aiSG/RISWELbyYk1XykIZOVmIfpYNBSxCnhO90SV+CoYsHsALj6/iOLzmUDY\n+mvrbT6fIbOE077WrF5t8ngNr0H9qYx44NOvJ14nW/fK0z6kYcSBEZDFyOA53RPxF+JtYvBoNBox\nBFRreS2b70kX6enpqFe0KGRE2L12rdnjcnMZi5IIKFTyH1DzoUzBlngUrnAZJ67Zlg8zrNo2xeBa\nsUe7ALgn4NTl/C0As8aOZQtAhQpGPQKOHAEUPcLRNn4gzj2+Aq+IgaBvwv//TfgSVQFHPDrSYxwi\ny0bT4MbNvnRx6xZbAHx82P8/BhHLokHF/mTeftBR1rlIJ1mTkZ2B0FGDIf1qHI4+OmryHIcOHRL5\nzwPkcqQ+fGhRt/1TIePpU/TWPq+wsDBkm9kBvH37Fl9++SWICG5EiJVI8N4eFTUAOW/fooO26GeF\nQUMaXagmq5gctbZ5Bkk/gNr1sCsmGqzVmbG2k0pMZPkGR0d8tNjd30//RtjiMJCa0HVrV7x4r284\nMnMy0XB1Q8hj5Dj4wHZ2ji6zZFDTpihMhKKFC+OZiW5uANtpdfw5hjFL+rXBo2emt0k8z2PTjU3w\nn+0PUhN67+iNV+n2jTmNRoOyC8uC1ITm65rb9V4BKadPozIRVHI5jh0zP/dVk1Wg2tMhNiuSZIG+\n7mPXuOjevbtNO6mVey+CHF9D5p6Y/wVAOwY7deqkN68MGUcpKUCJEizBffas/Z/zWRj/LvTYJh19\nAW/evLHZ+AP6C8BN22pj9PDuXSYkX41kTSqUKaCv++Lhm0emry3jDcotKgfXqa56OizJycn47rvv\nQEQICQjAcSIWxygACIPKUP7XmkehCQ3FBO0zCwwM1KvErFWrFsaNGycmiRvL5UgiYnKa+cCH9u3R\nTC6HRCIxKWeg4TWIPhbNwiQ15rJJXj8Gnbd0tjkm+uzZM7Mdm0yF0ZKSmEyFg0P+2gK8z3qPYfuG\ngVNzCJgTgF13d5k9NiUzBRWWVIBznLPVsIpJpKXhklwOF4UCYWFhSLbAhRy4ahEoioOsT30sW5Oi\nRy1cvfsOSsawsE2lXyrhzNMz9l+LFtm52WLBV5ctXay/wQRehYSgrIMDnJ2dce7cOaO/52pyMfHI\nREjUEiaJQoCsYZxdsfKXL18iMDDQ5jDaqv0XQY6vIHNLwl+X8rEALF6M2drzGy4AhkhMZP0pChWy\nj90EfAbG35PKi7o8R2ww/i1atLBo+E0Zf4CFfXx9Wbm/PQtAvamDQEXOMWNUejvqzzPfe1TAk5Qn\nCJgTAL9Zfnj09hG2bt0KX19fSKVSjBszBpkVKgBBQVqhmY+HQDkNmh4KUhOCZ4TaRiHbuBEgwiAD\nOqzuSyqV4ueePRljR2gtmR9s3Yp0ItQNC4NcLseePXvEPz1MfijGnAsP6AxyfAlJg8kgx5fwGdQF\nuRrTuQIBPM9jzZo1cHd3N3sf5nIoL16wxKKDg23MEgEHHhxA8LxgkJowcPdApH6wrkv/PO05guYG\nwXuGN+6/yQeTq3VrHPb2hlwuR7169SxKZET//htrhdnvC9Se0ROSaAlKzqwEmiiHU6wbFp5daPW5\n2oL0rHT4zPQBqQn9d/e3/gajC43GMyIUCwyEp6cnrl+/Lv7p3ut7qLOCKWb6DOoCcnwJacMpIMeX\naBNnW9hx27Zt8Pb2Nit+aK5q+9eDF8E5vYTU9QVOXGQLgKHnDsC0k/X6NSCXY46WsdWxY0eLC8C9\ne6w4OCjIJi1HEf954x9CIZhDl+FKWYgg0wqHAGu2YPgFOhHBQft/ifZfZ2dns+fQXQBuWBEuvJP0\nCFQ/liVzHV+A69QZyRm2U0VvvLgB1/GucK7IhK4qVaqES5cuMSIzEbBhg83nsgZLVZgWwfMs7qFU\nwtkM5TQoMJAVrxDlCd7nBxkZgLMzUiIiULlyZahUKhw9ehRLzi+B0xQnuE51xaj4vVC4pKBt3Fxc\nSbyC0FGDQY4vET71Z7N5iaSkJLRt2xZETHp3woQJcNEZF1Ltv9+YEXEDWD6hfHlG0T5oJSrzOv01\nIv6IAKkJpReUxsnH9jHJ7ry6A6/pXig+v7j9LI9ffwWIsHHyZBARwsPDxVaHpiCLlpscE8pY4xaM\nH4PkjGSx/+zYP8fa9+Y7dwAiPIyMhJ+fH3x9fXHv/j38/PfPcJjsAPdp7hi37KDeuGgbNxcKlxSL\nzk1ycrKYNK9UqRJWrVoFL5nMyF7MNlvMAqw5eElcAI5fTBKdLK0+o8k+DyLatgX8/DBn1iybFoAL\nFxhLuUIF2yvfPwvjf4SOIkKrxT9njvFN9ujRQ88gCdW5qUQYT6xIagQRQiUSODo6mtw+Crhzh7VT\n9PY2vwCEjOwFKnSTefsVV6PHumGmDzQDnuexYsUKOLs6g2SEou2LIiU9BXj/HihShLWLKiCaYXo6\n0HrYfublCRM8ihA8L9g2+d9u3QAiVNTy9Q1fNVxd2fBo1+7jL7Z7d8DTEy+fPUPJ0iUhVUlBfQhN\n1zTF45THJj2rjtMXgZqMwqxTs4xOt3nzZnh5eUGpVGLmzJnMED54gPcSCYZpx0VnInxVrRokEgl2\n7txp9tJevWITT6lk7Y4Nr+PwYR7dpmxE4ZmFIYuRIfJwJDJz8rdz+/vp33Cc4ohKv1RC2gc7hL+S\nk1lRxKhRmDdvHogI/fr1M7swJqQloN7SpmLbQWmU8qOohZaQkJYgCsJNP2lnBrNiRaBmTdy4cQMe\nnh5QFVKBhhNarGuBZ6nPbPe4tdi7dy+KFCkCmUyGqKgoZnQTE5GuUIj2YqRUinLBwXBxccnTvjKB\nNYcugnN6AanrC+w9/gItWrDp0KyZ5Q5v2LqVHXjgAObMmQMiQocOHSwuAAcPsvh//fq2BQX+88Zf\n4Pnn5gLt2zNan9DoKCMjQ6/zEMdxeCM0gPXxYZctvJyckJiYiODgYBQuXNhiVezdu8wGOzkBK1bk\nyTO0/zUCVOZ3lnB0+xfyiNZWVQYN6WM3btxA48aNQcSKiBbuXQhJtAQt17dEbtQkdq1//WXxnLbi\n/Hmg8NcL8wx/VN7LeXxJ207y5AlAhGZmirQ6EzGL+P79x1/wzp0AEQ78PBTOY53BeXBwcnPCtWvX\nzL5Fw2vQcTPjlm+6wTRvXr9+jc6dO4OIULVqVdzUjeN168ZiONu2sX9btMD79+9RtWpVq47B69dM\npFRbvIwt+1isPH7HRci+Gg9qMgpVl1Y12VTbXuy9txfSaCmarGmCrFwrmtq6aNaMhd94HmPGjAER\nQa1Wmz1coBbSBBVokgRf/Zx/aqE13H99H8pYJUhNiL9gQ9WeAK2G0fpdcXAY6ABSEnyDfe3ul5Ca\nmorevXuDiBAaGooLF3RyKwMHsoXz55/ZHBwwAE+ePIG/vz/8/f3x9OlTs+dde+giyDmJMftUyfAM\nSgD1qo+vu1iQZ8jMZNpXPXoAAObOnQsiQtu2bTF69GiTdFMA+O03dnnt25tlRov4bIw/wCIDtWsz\nW9Oxo75kQJ06daB3oGD0f/uNLZVhYQBYdygPDw+ULl3aSBNcF/fusWgGxwHUpheoezOQUyKIeFCZ\n3xG9z8QWxACG9DEhOero6IhffvlF5EDHX4iH7whCpkoGvkMHq+e1hpwcYHx0Kqh3DVYsopaBxrkh\neEYovvvjO7YATOIwZLT1LmAAgJIlcZQIEoOCMyciHCMCbKR1WsPzV//gnaMMqysSGqxqgKOXjopb\nfXNNTQDGlqmzog4UsQrELYuDj48P5HI5YmNj9el0Qv+GcePYzxERTNktOxtJSUkoVqwYChcubLKP\nroDXr4GSJbXDq/Q2UBQH6tEE5PgS/X/eZHNhky1YfXm1yBCyhWIJgAkjEQGXL4PnefTs2RNEhHgT\nxVK61MJ1f16B5OuB4LqEQyfdUuC4kngF8hi53mJtDQlX/gKIMLIpocmaJti8ZzNUKhUqVapksqew\nKRw+fBiBgYGQSCQYM2aMfuOfBw+Y4RcEE6tUAWrUAABcvXoVLi4uKF++vMnPysoCxo8HyO8SSMX0\ngqTV4tmCWmMunJwseP99+jAPU+s4CTUvgkNrTjBw3jz2FffrZzlA8FkZf4BNPiJ9xoYRtc3RkV0y\nx7Gf585lP2vlEE6cOAGlUok6depY9Nzpx5Igh5cQKWRcDqjFIOuxci3MFeKEh4cbHXuxZSVkSQiz\n1nyc53XvHlCqze+gSCYhUHJeCMZPfa43AAftGsJ2AsMCsXe/DcbqF9bV6m2nTiLl9KvQULwnYlmo\njwxR8TyPdVfXwWOaB9ZUkuKDkwqaDObx3LhxA15eXggODrbofT18/hCu1V1BRCgdWtp0cVnz5szY\nC0HTP/5g40Kbyb1z5w48PT0REhJi1jFITQUko/xAnnfYuCi/Vizey28ZviVMOzkNpCabmqcAYAkK\niQSIjATAtIBatGgBiURi1DPaMFyycyebMmXKfHwbT0s4+fgkpNFScGoO++/vN3scz/NYdXkV3Ka6\n4YK/BC/KBIrPYM+ePZDJZKhbt65FUb33799j8ODBIGId1k6bkBERd4NCNnXKFDYutHbl4MGDkMlk\naNy4sV7L1Vu38uREvv8ekP4YBlK9YpGBagvFcSGPNjMuTpxgb163DoB9hXtjx7K3WtjUfV7GPy4u\nTu+hyGQ1kGgYnnzzJs/rF7bwjx6xn2fMEA/btGkTBKqVuSpElzgX0I8lQC5P2SnrThbVOW2Brdxh\nXL0KnuNwqG15kJrw89/2F3DxPLBwcTYk334NimI665F/Rpo9vskqpscu6VvDep5Wo2Gi/U5O7IN4\nniVFiFgmygaYi8tOmJaE8I3hYkHQ001az1WnfeKFCxfg4uKCMmXKGHfqArB//374+/tDKpXCsbEj\ngmYFGcetjx5l5505M+936els0g8cKP7qr7/+glKpRO3atY3YMidPAkHBPKjWbNDoQtqdIEBfrETN\nmZ8mVs7zvKicOeOvGdbfALCmPGXKiD++f/8e1atXh0qlsiplsngxe0z9+xdY2skk9t7bC07NQRot\nxaknp4z+npCWgNYbWEvF+qvq43X0WD0HDmBzWCKRoFmzZiZbeP71118oWbIkiAhDhw41vUgY7gYB\nZtWJ9GjLq1evBhEhIiICubk85s9nBIBChZgPAQCR0xIQOr6XKOcirTUfss7d4R6QCJMRKo2GOU9a\nATt7jD/PA999xy7TnLzFf9745xl6/Zjzb79dh5MTW3nTdHNiMhm7XJlM/0lUrswauOhgljbTPmLE\nCL3fP3r7CJyayxNmc3wJqh9tlzxDQkKC2R7Ael8mzwNNmwIeHsh99RLtNrYDp+aw+YaJ1n1mPwuo\n+c1J0Bg3kJrgPd3Xap9VnudRal45Jhz1bVdYcKoZ2rRhz3XrVuZVEgGtWtl8jQLzYekOVmuwbOdV\nuNTYDLcphaCMVWLGXzMYtTA7m8XbuupTZo8fPw6VSoUKFSpg+PDhcHd3x8iRI/H999+DiFC2bFmc\nO3cO55+fh+MUR1SJr4J3We+Em2Xb+IAAY/2H8HCW4NFxALZs2QKO49ChQwfk5uYiO5vdMuf8Eo7f\nt2Pj4psOrLWmMgVEuaAvVmDrVpsfh13Q8Bp03tIZpCasuWLcZMYIixax70cn1/Hq1SuEhITA3d1d\njy5pCmPGsLcXUJmJWfx2/TfW3jFGjs3XN8NtqhuuJF7Bhmsb4DndE6rJKsw9M5eFvB4/Zhc1ZYre\nOZYvXy7GyseMGQN3d3eMHj0aQ4cOBcdxYmtNs2jRQn83KKB0aaBJE71fxcTEgIhQrNhEcfgbOp+t\np8wBqZJZoRmXjRpDfoaDA+NwmNygREaynVpCgl3GH2C7s1at2Nt//93475+N8RdexYoVE29uzx4m\n7NS8uVaC5ty5PK//jYGWR2ws+31CgvgrnufFLaEglyD0KdUz/D0b4ovFX4g/W+PH//777/Dy8oJC\noYCTk5Plph9797Lr0sbNM7IzUHdlXShiFTjyj3Ut1y1beCg79hEZG73++M5mQbLMnEx4xHmDogje\nnaJgMXx6+za7ztKl2cKqUADv3tn0OQKOHAEkg0PZtY5zZWJjS6vi5kuDwgohFmowWwRxLIHSK6hB\ndu7cWS98t+vuLjGJnqPJyQvvLF9ufFFr17K//a3f+ERgYPTsOYyJlJbaA4eJPlDEKlDj+02QOTNq\n4bbjtyFVZYC4XHh42P1IbMaHnA9o/GtjyGJk2Hd/n+WDExJY/CYmRu/Xjx49gp+fH/z9/fH48WOz\nb9domOw8Eetr/Cmx5PwSPZqpS5wLSE2oubwm7ry6o39wrVqMcmWAQYMG6cXKhXHRunVrpKVZYEsd\nO2YUERAxdiwzLjp2ZONGHgrFD1qDvMxoZ3TkCETKabepa5lTIM3CpEk8OI6xO42StMK8mjPHSLJD\nuJ/x48ebvYX0dKBmTZYHNSyC/uyMvyGWLoUYc+OFTiOOjsZP6cYNmNoj5ebmol27dqyhSReJHtfZ\nXgpZWlqa6IlWqVIFd+7csdz0IyeHlZCWLKnXJT05Ixmhi0LhOtXVLHMkJQVo+/0d0E9FQGqC82RX\nHHt0zOSxlpCYlghljAMoilCuy1rLzdqLFs1bXE3kLSzBrlqDw4fZZ2zR16G3xzMSjEr/P3qDL1vW\nfCBboEeOGaP3a54HGjUayiZhBaZbX35xeVxNumo0Lk6eZHaCiG3iCqLTlCmkfkhFpV8qwXGKI84+\ns1LvX6eOSUN59epVuLq6omzZsnnMOBP48AFo0IBRCz9W3sIS7BoXQqbTQIjLXo8ZAPuCa9ZkDZ1N\nFcOdPcs+69df8fYt8O237MeqVbNRt24zSKVSo8ZOhuOizeg/QAT4lXoukogGDTIRTqtaFahUoU+p\nagAAIABJREFUCYC+ZMfYsWPRsGFDyGQyHDp0yOytvH7NpEhcXfVLbT574w+wTozNaTurMiUy/WXy\nPFCqlJ5MsYBhO4aB/AkkI9APhD47+5h90OZw6tQpFC9eHBKJBJGRkXqJIbPQJlJN7dmEKmAXtS82\n7H2k97c5cwBlMzVr/hBFaLamBbJyLH+eJcXCywmXIVFLQZMkaN7nlPlYb/v2ecafyDaRfbC5WqL5\nLtBEnaYfUQTFmKK49NwEhzo3l1F1DZhP9k7ysYfGoldbMvuMRTRtysaG9saTkrRqkX5nIA1hRXit\nJ7S2yNvfujWvy1mvXp8uXp74LhHF5hVDoRmFcPe1hXp/A5KDLo4ePQqFQoFatWpZTJYmJzOj4uZm\nvegxv7iccFmUgBBeQXODTNegPH/OHrJBljNfxl/YDS5bZvrvGg3g74+XdduhaFG2uKvVzH9IS0tD\npUqV4OTkhIs6PS8MwfM8KnVlyr4tv7+MkSPNbDTmz2d/MPGQU1JSUL58ebi4uFhUyH3yhEU1fX1Z\nihP4Hxt/ImpORHeJ6AERjTVzTEMiukJEN4nouA3ntGr8eR7QaA1Sqqu/2QeE0aOZl6cT3xO2mTSK\nQB4Er0JedjVJyc7OxsSJEyGRSFCsWDH8ZStHPzWVlRLXq2fWUtx8eRMukz0gHRqC2duOwHWqG+pH\nHAYNZIJZihglNl23TpezRbFw++3t7DlMUGLQhEemT9S0qb7xN7GQ6oLnWT90WcMp4kKla/xpvAqt\nwt+Zvv1Bg1hGTWfbbu8k12Sk41UhR/ztT1hrKVYuZDlv3MCOHUChwjmQfRkDiVoK/2n+KFe5HFQq\nFU6dMk5M6kLw7oiAqCiLh34U7r2+B+8Z3gieF4zLCZdNt4IUYuRmAvdCXqNFixZirNwUr/zff5lB\nCQy0T1rAHpRbWE7P+LtOdTVPba1fn61IOoPGbuOfm8vOERIi7gYNvfYPH4B9JQYhnRxQvkS6kbBa\nQkICAgMD4evrq9flzdDJepuagoBGe0AEDIi6js6d2deycaPOyV68YKvLWNPVz0+fPkVAQACKFCli\nMVx34wabMgEBjPT1PzP+RCQloodEVJyIFER0lYjKGRzjTkS3iChQ+3NhG84rfpkODg6m73riRPBE\n4IkglQL7zbHHzpxht7JuHbZc36I34Gotq4V79+6hUKFCKFGihElWiSHu3r2LatWqgYjQq1cvpBp2\niLKE8ePZtVgoKgKAvx7/BUWMilE3o0g0oJWWVMVbG+UkbJ0cs07NZucf446f403ESuPjbfb8nz8H\nGrZ8CRoQpq0eVcA51h2hi0Kx6cYmBM4JZJ/17VeYoDaxaxFocDpBZ8OYqNXG6bNmAUQYNqYi5DFy\n831lnz8HiPBHlViQ5304DqkJUhO6/94dbzPf4tWrVyhVqhS8vLxw14q6ltACmYgVCH4qnH9+Hk5T\nnOA13QsStZlWkNWqsZCCGQwbNkwvtmzueV68yFIwlSoZkCsKCH6z/MRxIegADdk7xHTuSkhm6xT+\n2T0uVq6EYVhRV4rh6lUmptaIWPgxc8M2k6e5efMm3N3dUbZsWSQnJ5t1snYf3A+3CsdAXC7ifrmP\nevVYykzv8lq2ZGFVM8zD69evw83NDeXKlbMo2ic4IKVL/2+Nfy0iOqDz8zgiGmdwzEAimmzXB2sN\nVUhICORyOQ6aElfRzrbs0Ir44gumgWFyN6bRAH5+2FfBUc/wn32St6yfPn0aKpUKNWrUMLsl5nke\n8fHxcHR0hKenJ7ZssbNH6pMnbInWNje3hHzr8uigY8eONntGvbf3ZecfWgy7dpsoIYyPZx6/BcO/\nZQvgWHc5aKJcVId8k2EcX152cTn7rA5dsWixwaDXbrvRpo3er21unJ6SwhoAN2uGt5lvxRzK9RfG\nTJe//wYuqmrhgmMg5FFOcJ/mjo3XN+od8+DBA3h7e6N48eIWK0s1GkZUImIRin1WcrP5hU2tIKdN\nYxdipv+wPR7z3r0G5IpPBJ7nMXz/cJCaMOXEFOMDkpL06hgE2DwuMjOZka1a1WjHfegQW+QkEvbd\nTY3NYWNIW4VrCseOHYNCoUCDBg3QtWtXs8/zYVISFEGXQLIMrNzyDGXKsDaUooy8ULprIcFy5MgR\nyOVyNGjQwCS1VcDkyYJJLJqA/5Hx70hEy3V+7kFECw2OmUdEi4joGBFdJKIIa+cVeP5v375FhQoV\n4OTkpF+CL+yjtCGh58/ZFlU39iXgduJtLKpKeC8nqCIJfrP8TD68bdu2geM4tGvXzkgY68WLF/j6\n669BRGjatCme52cv/O23LD1vpSn4zZtAUNuVRro8PlNt1OUBa86uVCrt2hY3WNGY1QD8UM/0ImoG\nKSlAl4j3oO/qgaIIkmipSc0dXUw5PpXdV/Oh2LzZwNMbNoy5SPnprSzQUS9dAgA8TnkMv1l+cIsO\nwKZ9rHgnJ4fFcTnnFxhduRxAhC6za+Fpqmne69mzZ+Hg4IBq1arhxYsXZnMoWVksWUrE1ngbSyHs\nQkJaArpt7QZFLOv7KomWoMuWLvrhn/v32UWYEsSC/eESoXj4hx8+bQ2Ahtfg223fgtSEZRdNxOQb\nN2YkifxcxOzZ7CYMJFrv3mXqAYIp+ekn7R969mRW2sKKt2HDBhARCptRvxWe56nb9yDxegCJYzK2\n7U2Gjw+j+SckgNF2XFwYc8UC1q9fDyLWEN5SlzTG1qoC/D9k/BcS0d9E5EREhYjoPhGFmDhXXyK6\nQEQXAgMDxRtKSEhAsWLF4OXlhdtCxl/4tr7+Wjzu5k026QIDge6//QBSE/xm+IGiCE16sOMPzMor\n7DGF+fPng4jQv39/jB07Fu7u7ujUqRO8vb2hVCoxf/78fLWow/nz7HrNxPcAFpKcPD0d0pbDmXzA\nBH31RcmPoVbppm/fvkVERASICCVKlICbm5uelj3Hcfjtt99Mvlej0aD4nFLMk+zSy9oaBYDRzArV\n2QEaz3ZVxeYVx79vrb+R53kM2jWMhYYaxOnr5gthOjPdqcwiMZExvrroa8hfTrwMh1hnSAdXwISl\nJyGNdANVmw8aVRhlhigAImisSFXs2LEDEokEMplMDJdIpVJ4enrqhRlSUhiRi+OY82hBSirf6L+r\nPyTRElEuwWTf3IoVmVUzgfwkSidMYF9JbGxB3okxsnOz0Xxdc0iiJdh2yyDsIlD87PFMAPaleHmx\n3JUWGg3LjatUzOt38k1A4KT68AxkujzYvp19lgU515SUFFStWtXkszR8nhtPnAc5JUFZ6Dl27M0Q\na5XevQNjCri6mu9FqsX06dNBRBg1apTJvwshrP+l529L2GcsEUXr/LyCiDpZOq+hvMP9+/dRuHBh\nBAYGIr1iRT2vXxcC2UFXzIyiCI6THNhK3rOnxQcMAN988w2I8jR5hIm+2l5jJIDnWcLK25slfE3g\n3j0grMUp0I/M+FaZNAAeU3zFmGjoolB4TfGz2Ihl3759YsXrxIkTkZWVpbct7tOnDzw8PFCsWDGz\nXZ/Ss9LhFucFiiIUCo+DuTDjhw/A8JHZoM5txcriUQdH2dX8XMNr0Ok31mNWWXuZ4Kyz5xUUZKJL\nvBUIIl0mEvd77u4HFyUFjXMS8yclZlZk4aCwMPb9WEGpUqVMTvKmOkYFYLtQX1+2ABQvzih5BYnw\njeEYuHsgriReEXXtu/3eTV+HPyaGTQQTO1RzvHLDokdd8DyLghAxBelPifdZ71FjWQ0oY5X6NObX\nr9n3O3q0fSecOJFduHYrdv8+ULcu+1XNmmyRbhM/AJJoCdrED0ChQsCxfRnMkRhgWnblwIEDCAgI\nAMdxCDDTItSwpem0TX+C5O/gFvwQGzfnQiJhQzz3oJbivMkyicNUfZIA3dzF/zLmLyOif4ioGOUl\nfEMNjilLRIe1xzoS0Q0iCrN0XkPjDwAXL16Ei4uLmOTN26PlwWKs/Ntv2TdtRcDEZnkGWyHQyxYv\nNvqTRgPM/jkDspY/gaI4FJochD8f2tc+Ki0tDX369AERq3g9f/682WPPnTsnSiaYi2E/TXnKks1R\nhLIdN8MwzHjtGlC8/mnQGKbV7j2jMK4m2haOMkRWbhYaLG8GmiSBW40/8uy2wNCy1XIKIl0mJuvN\nm9BnHBmOi4kTWcDXQrJ/x44dekqyui9TDWFu3mQ5KI5j+VcrTt1HIe5EHEjN2i6Ki68gVbBwocn3\nGPLKv/rqK3Ach7UW+uZmZbHIi0zG4uSfEq/TX6PswrJwneqKy4mX8/7QvDkQHGx76Ccpibn233wD\njYaxKx0cGI31118Bmdp0DkWmVjGKs5+fXjI2NTVVb66dPXsW3bp1MzkuAgMDjfr19p3zB4jLQWDl\nW1i0iGmH9e2tAR8QoBfFMAfd+qTfdWjMuqyl/5nxZ59FLYnoHjHWT6T2d/2JqL/OMaOIMX5uENEw\na+c0ZfwB4J1WW0ZDZJTcuf/mvj6lUOffvjv7MjlfIqu9+fLFHzaHrCzGJS9b1mjRefQIqNz2NGhw\nCEhN6LGxv3067mDJoODgYHAch1GjRlmVmgaY5+fg4IAvvvjCLIPg7LOz4NQS0CQJqrQ5h2cpCai3\nsj4mTk8A1yavsjhiW4RV5UlLtQYA8/QqLqgBmqCEb83jLBZ68SL7rpYute1BdO3KPDWdSu7sbBam\nkPvch7R/daNxUTgukOVQBJ0XE5XAL1++FGWidXeC1ow/wIhLcjk7tckqzwJE5OFIY7ZM2bJAw4Y2\nvT8jIwONGjWCRCLBVgt6FSkpbKOkUBhT5W1pEWoPhJoXn5k+eJisVVwVmh4ZVGabxeDBgFSKJ3/e\nRf367K0tWzLttmepz9B8XXM9oy+NlqLb791YDkWoAj/DWloeOnRITyFUmGvm7AURoUePHkZh4q9+\n+g1EQJXmNzBuHPuIU/W0zo4NbMOMjAzUqlULKpXKJMX8f2r8P8XLnPGH1uv/hggtW7YUmyCUnFdS\n3/BH6U/0lLScPEGvwYMtPtwCM/6nT7PVnAjYvVv8Nc8DC+MzIG81EjRJAs/YQBx6YEevQDDPTZCC\nLVmypO11Blrs378fcrkcNWvWNFsKv/nGZvYMI1XwG9JRy9F3AKkJDjGuZpvR68KWWgOAeXrBM8uA\nxrohpN5VpLzlWXKvcWPrN3PpEnvGOuXwFy8CFb7IBtWdCukkFRxjXMGN9NP38CIdsPNgCvtCgoP1\nNIt4nsf69evh5eUFuVyOmJgYsR+D4cvf399sHmjLlrwiMJNVngUEXbbM2ENj2QIwYYLVHY0u3r17\nh9q1axu10zTEkycsxCCR5EUqLHav+gjcenkLntM9UWJ+CSS9S2IkAIUCGG6DyOLDh+Dlclyv0w+O\njiysvnIlkKvRYNG5RXCJc4HDZAfUXFYTkmgJZNGMYBGyIISF0N6+BWQyfBg2DP369QMRoXTp0jhz\nRr+/sTl7UbFiRRAR+vbtqxcO1fAahHbeyJyC3jdQqRIQStfZIGndGjh92upCKtCQPT09ceeOvhzG\n52n8XVwgxPrj4+NBRKjzfR39lVutEI1+6fmlxf+7/tAJr5OzWeepgACLs9Bu/rApnD7NFhoiNku0\nhULPngE1O54BDS7NYrUb+tnt7Z86dUpULfzxxx/xPp8NVbZt2wapVIpGjRqZ7fsqi5GZ3BbbKmNs\nz0L6OOUxCsUFgEb6onqzf5AzRit+lZRk+UOaN2fhvJQUZGQwxQZJwDnIBlcEqQntN7VH5LTn8JqS\nxysvOqcoSE0oGl0dbzPfMmOiUABpaXj69ClatWoFIkKNGjVwQ1uBefz4cXh6eopig7rtQ/v162d2\nARAKOYn0xUULGjzPo9+ufiA1IfZ4LKv5t2f3BJbErFy5MpRKJQ5b2CFfvcqGt1QKjBz5aQy/gDNP\nz4gdzlI/pDID6e9vlhsv4F3b7vggUcGPnqNZM7ZoXX9xHbWW1wKpWY+AB28e6OVQqi2tBlITumzt\nghxNDl5XqYJ/ZDJwRBg5cqTJeWJoL4RxMXz4cIwfPx5EhCFD9OsXMrIz4dtgB4iA8N53UVd6Ghri\nwBMhV+mA5m6nrT7Phw8fonDhwggODkaijtLc52f8dSWbtfK08olyvVju1ONTTeryfBU1G6QmuPdv\ni9cLl7NzWCmyspk/bA5xcXkun1QKfkoclq/OhPLrUaBJEnjEFMX++1YawxogMzMTo0aNAsdxCAoK\nwpECmG1r164Fx3Fo1aqVSWkKoQOT4cuWfq88z6N27dp27aJuvrwJpxgP0I8l0afZUfb8dCR2jaAj\n2XzyJFCy3DtQ86HgoiTwm1kEf9z+w+xbd97ZCUWsApXjK+PN4d0AEQ717g1XV1c4ODhgzpw5RpRf\nw3ExdOhQ8Z769u1rdgEQSvyJGLVbQEGHSjS8Bj229QCpCXNOzwZKlBClg23Fq1evEBoaCicnJ4s7\nyoMH2dpMBAwd+rFXbhn77u+DLEaGRqsbIXPNSj07YAiNBtg0/go0xGG2YgyWLWPGdsLhCZDHyOE1\n3QtrrqwxS04QeikUH1scvaXsS7tsIRcCGOdQWrZsCSHpKxTVjR07Vu8zX6YlwznsMIjLxbjGy5FL\nzF7kkBQPesfZ9FzOnTsHR0dHVKlSBe+06oKfn/FXKERDOmT3EP0QzzDCNCs6tP1WLASpCR79miBd\nKdHX8S5gTJ8OXInbjQRnQv1ehAQvFdrWXwYaVAakJnyztg/zYKxAN1b+3XffoUyZMiAi9OnTx7Jq\noZ345ZdfQMSamRsau4S0BPhPK6kXQvOfVsqqhv3169dRr149s7HQ6tWrm33v6SenIVc7gPpWxp3C\nIeDNMXG0ks0a/wAM7ZcJKrUH0pGBIDVhwO4BSMm03u1p7729UMYqUXZeGdxzk+E3Inz55ZcWu3rp\nXwIvendEhN69e5tcADQaxvwRNoLHjjHD7+oK9O1r00fZjBxNjtji8pfxX7FYsoXqUFNITExEqVKl\n4OrqapZAcOQIS5wqFOyeTImnFiTWXV3HdnLr2iDXQYkLtQcbOXobNrBo4W5qiTSZO55cS8axR8cQ\nskCbV9vWAy/f64fBDHNS+/btg2crTxYCGhOETBkZKaVaQ1ZWFjp06AAiwvTp09G/f38QEaKjo/WO\nu/XsMSTuT0GSD1hWqD7q9yI8c5HhyIIbNjsFu3fvhkQiQdOmTTF69GgQUS4+G+N/7RqEJK9iIqfn\ngR5/eFyssFtuZfQNW7sUFMWhWi9PpJYOse3J5gNHjgCLVD+hfysCF0VwHlAKNEkC54lFsffuAZvO\nYRgrJ2Ic/RmmZGgLADNnzgQR4bvvvtMzXkeOAJLhwSA15RUWDS9mdkualpaGESNGQCqVwsvLC6NH\njzYKoQmJ0+joaLPe1557e8BFSREcUQwZUjItMKNN4A/wnwPq0AWkJpRZUA5/PbY9/5Gbm4u+0/uC\nIgn+AwlPPJXgbemSbQC1Wm11Adi/P08FVC5nJBQ3t08TLsnKzUKr9a3AqTmsqUD210wAePLkCYKC\nguDp6WnUT9lQFkFofWqmrqzAMPfMXJCa0O/HYsj09EFhr1wcOcL8gJ9+Ys+2seI4QIQ3cZPQe0dv\nsRbiwAPjuWc4z4SQnr+/P4ZvYDmUFoPckVGlot3XmpOTgy5duoCIEBMTg169eoGIjObwrBUPQVwu\nSJ4KGhSChjXb2D0uRowYAaI82i4+G+PPcbjvSfAdkWf0v1jyhfjnrKwsNGvWzGTLOkOM3bga3CQO\n9XsR7u43T4n8GKg+IlQioMDppjYgKipKzCMIRnn6dKDugryY6MDdA1F3QbiRV8LzPDZt2oQiRYqI\nuxOhJaJhqCQlJUXsMTto0CCj3YaAX6+sAakJHTsRNtbTtyo7fs/BA1UZTP7CF9JxHpBHKxB9LBof\ncsyXvxt6eOfPn0f16tVBRKjZtSYcoxUoM4jw/A8bGqeYwJQpU8Tv6YcffjC5AAgtE4UQ0KcMl2Tm\nZKLxr40hmUTY3MMMgcIKHj58iCJFisDHx0dP38gwvPr4MSuulEoNxMs+AcYeGgtSEyY2IlyafQSe\nnkyThwio9AWPjMo1samuB3xmFIY0WopRB0chPdt02NZcTqqLtlBw6YWl4NQcmvQgpN+/ZfIclpCb\nm4sePXqAiBAZGSkyxxYsWCAeo4hRgcK7gUgDUr5lPYF7NoQixvb2oIb3gc/B+N/8dS4mNSAoJpBY\nTGRKL+bdu3eoXr06lEql1cTs5FULIZ1ECOwbiMu3bGxmbiMSEoAZDdroLVTSKCW6/257u7/z589b\nLRn/FOB5Hj/99BOILDeSMMTdu3fRtGlTEBEqVapkxIYw91mjRo0Sw03mNEtmnJwFUhPCW/lgyITn\nqL+qPkaoE9HRMw5fRrDnW3t5Xdx6aXliGnp4wu7Dzc0NGzZsAM/zOHH/TziPJ5SMdMWTlCc2378u\nhB0UEeH77783WgCOHGFVpYLxJ2KlCZ9KN+d91nvUmVAEsomEXZdta5xuiNu3b8Pb2xsBAQF4ZKid\nooM3b5hYLRFTU/hU4Hke32+NAKkJP4XXgcQtAdSrPhq3TcS/m5ahVTc2LqrEV8GlhEsWz9W6dWur\n82z1gengoggNY0vmdYmzA7m5ufjhB9YMZuTIkWjbti2ICMu0XNnIaQlovKgbuCrL2JiQZCG0/QhM\nmGZ7e9DPyviTH0EVo4LPSPZFFvmJEPF7hMUH8OrVK5QpUwaurq74/vvvzfLKAWDdVyUhm8hBPrAa\n/r5qXzzUFN6/B4ap/4Vrx3BwUQT5BAkoioMsSgWKYpWD1vDvv/+KHr+9ujwFBZ7n0bdvXxAR4uIs\nJ53S09MRGRkJhUIBNzc3LFiwwKwXbw5Cl64mTZqYzWOMUNdnYac+5UFREnA/VIUqkuAaKcUv55dY\nrTMAzHt4HTt21DvudK8mcB3Hodi8Ynj09pFd9yJAkAgxDKMJMX43N6byIdQAEDFNIAs9Vj4KKcf2\no2ofgjJajkMP81eddeXKFavV4QDTT+vQgd3T8OFWCTn5QmYmMH1mDqp19QcXRZBENAZFSSDrWxtO\nkRwcJ3CY/dcMY8kLHSQmJopxeFvm2fqWRSGdRKizoo5N+TpDaDQaDBgwAALzp1mzZnpFda2nzAE5\nvoSk1jyQJBtEQOf+D2yuDfnsjD9FMaO//Auy+SFv3rxZj4JnlqY5dSp2hhC4iQrIBn2B4+df2fwZ\nusjNBeYvfQOn8J9AExRQTJBiVFMJXCIao208C5W0jR8IRY9wszG8lJQUjBkzBkqlEiqVCuPGjcOe\nPXs+nm6aT+Tm5opVi4Zl5AJ27tyJ4OBgcaIkGjY1tQOrVq2CVCpF1apVTUpqmw2jRctt/gxBlM/q\nYrppE84VIbhPdkbg3MC84iI7sXjxYvEzevXqBY1Gg7599WP8hw/n7QI4jilaGDSrKhhoNHgd7IOw\nsa5wnOKI/nEn7epUJ8CW6nCAzYkff2T39c03MKoQzy80GmDNGhZeIgKkE+Umx4UiSmb2HGlpaZg0\naRKcnJwgk8kQHh4ODw8P6/Ns0iRsDiXIYmSosawGowfbCZ7nRXZYv379xKK6qKhjYhvIK4lX8GXX\nH0DSDBABpWs+xJs31otDDOmm+M8bf+2XyUXZbvxt5pXfvQsQYcOE/qAJKkh/DMWBU1b45AbYuTcD\nvh2mgca6gaI4tJnbAU88JLhQ+0ebJld2djYWLlyIQoUKgYhVA+o2bfhouqkWCWkJpht/WEB2djba\ntWsHIsKSJUvEWPnAgQPRokULEBHKlStnuUm2Hdi5cydUKhVCQkKMQgsJaQno0MsDkknayT2B0LGP\nn033c+fOHTHpZtO4SEsDlEpcGtENntM94T/bH/de38vXPQlNxokIPXv2xLRpGpPjondvtiPgOFak\nfMA2ToB9GDgQSV4qhMwvBcdYF7iVO48t+9i42Lo/0Wae/okTJ+Dg4IAKFSrgyZMnZiu3eZ51riJi\nRcb5EWjVPdf+/UyrjogJov35JzAp9h906CIDF6UdFxMJnXp6YMLUBKNzCHNNCKd26tQJ9+6x79Wm\neaatAt/+8yDIY+SMHmwi/Gz9XvLCnb169ULt2rUhkYzBlCmn8w76+2/skTZEkbAjIEkWnH0TceGy\ndRKCcB//ebYP+REcIglf9iC029DO5odrLYGjh3LlgEaNsPavw+AiHSEZUho7jpjf0gq4fDUXYd+u\nBA0PAKkJlWe1wrWk60z5ysFBT2LAFHiex/bt2xESEgIiQsOGDXHhU+j/ajFgNxOtMtn4wwI+fPgg\nqhYqFAq959m/f3+xurqgcPLkSbi7u6NIkSJ67JIjR4AqbWtBMomgmiSBZBKhSHgni8bqwYMHiIiI\ngEQigaOjI7p162abhwewquygIFxNvALvGd7wm+WH26/y55L/+uuvYmPxiIgIs3UADx+ydsPCLmDe\nvAKuBmaKX3j6WzyC5hSDcpIHqGMnUJQEyvYD7GKWHDx4UFQ3tfY8169n4a2wMOCpacVsi7h4kRV5\nE7Gk7oYN+qGk/sNLQRJFUKllkEwiDPiltd77eZ7H5s2bxaLIBg0a4Kxhey5bIFSBt2yJPff2QBmr\nRIUlFfRoo7Y6WTzPY8KECSAidOvWDVWrVoVcLkeXLl3YAjRuHDR+fuDbh6Pfol9BTomQKN8jfq1t\nldr0X+f5c34EySRCoZEEj2ke2HrTvN6ILswZfzc3N1y/btDQIzKS0RNev8aWcyfARTqDG1oCG/eZ\nbpn2/DmPrwbvFrtUBcZUx8F7x9gfb95ks3bkSIvXd/78eTRo0ABErFR8586ddilh2gObGn9YgTmv\n+VPlHq5du4YiRYrA3d1dLDCaPh2oP6s5BrYkXPEhDOwXYJJxBLC8Se/evSGVSqFSqfDTTz+JIQqb\nd1IrVrChf+kSbr68CZ+ZPig8s7DYEMbendT69evFBaBz586iVLiht/z+PRAeDjEP8P33TBqqQJCT\nA41XIVwJ7QqaYDqMZs+4aNiwoc3j4s8/WXF+QABgOAXN4Z9/8prjeHmxxdBU+Ch8Xi0xVD9pAAAg\nAElEQVQ2LnwJA3t5I3xjuPi3Y8eOiWyusLAw7N69++PmmlAFnpqKAw8OQDVZhXKLyonjoN+ufnY5\nWTExMeKCZBiqXqZUIlepBNLTsfrEIUgCzoMI6DnksdU8yn/e+JeVEfq3JPiMILHkus/OPnifZVnK\nwJQ0g6urKzw8PKBUKrFw4cK8AXDhArvFVasAALsun4FkvBtoWDDGTP1HnOQPXySiZZ+z4L5rwCqF\nJ5bEyr836w+kTp2YjKM2Zm1IK7x9+7aYzPX29sbixYsL3HMWkJmTieUXlyPk5xC9yc2pOTRa3ciu\n8E+BitzZiEePHqFUqVJQqVTYtWsX++Xp03nlpCoV+1kHz549w8CBAyGXy6FQKDB48OD8NdwB2Hco\nkTC1TwB3Xt1BkdlFUGhGIVxJvIKIbYxp0vOPnjafcvPmzeICILxM9QPgeWDKlLwFoEaNj5eEfvwY\nGDIEWCntjTRyRtuO/yAs7uu8wr2JclD7LmjWIRG2pm7sHReXLzOBTDc3VuBmDq9eMfqrXM420ePH\nMzE5szh+PO9hKZXA6dO4fv26KM8REBCAlStX2k1EMAmhxaiWy3rknyNwnOJociG1dTGdNm2ayedY\nX7gnLXX9ytM7cK25FURA+bqPLD6T/7zxryLcPBGyc7Mx7s9x4NQcSi8obZW+ZcrDe/HihRir/vrr\nr1likedZ9kinZeChGxcgGecBGh6A8uqu4NQcJMOKg9QE+XhvRO1ZiOxcA6MtaKhMmADAfOGIQqHA\n+PHj7ev5awdevH8B9VE1vGd4g9SECksqoNHqRpBES8QCLVITIv6IwOt02yzK/w3jD7DOaVWqVMnr\noxAXl2f8pVL2M4CkpCQMGzYMSqUSMpkMffv2tdjs2mY0aACEhoo/6inG5tNjFoS+DF+G/QAAYM8e\nFv8nYkbz5k37b+H2bdYnRCZjr1lN9gFEuDp5J5Qd+rOxHS1h9zHGA9KgM/D0ZKEVaw6yuXHRvn17\ns+/5918W2lIoWCdT3VBTejrrFKZUsq+5d2+mg2UNWWo1NFo7kctx2PTFF5BIJHBzc8O0adPMalbl\nC7m5rCdH5854m/kWs0/Pht8sP9Gx0h0TNZfXREKa5fCvACH8a/jaJ5PptZJMzniLcj0XgSTZcPdP\nxNUbpreFn4/x9/QUb+rIP0fgP9sf8hg5Zp+ebRPFTxc8z2P+/PlQKBTw9fVlfYGHDGGepI44miJG\nYXKSmy3SatOGuTTaEnpzEyM8PNz0+z8SN1/eRO8dvUUdnlbrW+HwP4fB87yeaFW/Xf1QekFpyGJk\nKDyzMLbc3GJ1G1wgInfIX9I5LS1NVNJc1bcvsmQyZBMhSyZD4rZtGD16NBwdHSGRSNCrVy+bJRls\nwrx5bPzdu4dcTS523d1lNMHtLd4T2FGGL3OS0HfvMgaQ4NTqCMNaxPnzTIqe45j3PGQI8/6RlQW4\nu+NalZ56hXst1rWAIloFiuLg+92PIEUa2rcHLBB6jMaFUDfh6OiIJUuWmM1tvHkD1KmTt4E7eJBJ\nQ3t5sd8FBdm+0B0/fhzNXF2RToRsIqQToSaxupHXBd1BR4u3PToh00EOTzXz+OutrGdW/FASLcHd\n13etntPcuGjq7GzUSjJXk4vus5eCHF9AqnqPNZuMaeqfh/GXG1P5Xqe/RruN7UBqwldrv7LLmAi4\ncuUKypYtCyLC4m++Ybepo2GekJaAr1a3ZK0U1QRZlIP5Iq1z59j7Y2ORlZWFLVu2wM/P75N7yzzP\n49DDQ6IeuWqyCv129bMpMXk58TIqx1dmxVMbw616KAXBOvqYpLMQX64rlWKs9l/hmXbr1k2v8rTA\n8PgxQIRD/ZqKCqDeM7zhNtVNb4JLo6VYemGpTbFkc5NcoVDg0iXTu9nUVNZ9UPCFpk417ZXzPPOk\nhWPd3NhG1Ig5GxHBDIpBMiH1QyoG7xkMTs3BPbooZKG7UKiQ5eZShuPi2rVraNSoEfuu6tbNa7lq\ngIwMoEqVvHsiYt6+k5PtkgYajQZNmjQBaQ3+WO2/n2JXquE12HtvL5qva47m3dkFz4lsjIsJrJ1k\nQloCCs8orBdedY1zhUucC5SxSsQej0VWrvnkjVAgafhqVr48ezgmWkkuPLgTXJGLIAL6jXymlwf4\n7xv/QoVYDN2EzgrP84i/EA+HyQ7wnuGN3XdtdIl0kJ6ejv79+0NKhLdSKVINuui0ju/PCkesFWk1\na4Zcd3dMGDoU3t7eovdTUMbf0GP+kPMBqy6vQvnF5UFqgs9MH8Qej8WrdPvqFHI0OZh2chqUsUq4\nT3PHyksrP0niuSCSzuakLr62ofORvcjV5GLPvT1o81sbnC9COB3AnIytN7ciOzcbwfOCmUNg4O1V\nX1odVxKvWDy3uUkuvHr16mWyzkGjYUVhgqGsWDEv+anRAN99x4w9EeDjwxLkZiOLO3YARMjcvt0k\nTfP0k9Mot6gcSE3w7NsZ5JSETp1sbgkAnuexcuVKeHh4QKFQICYmRlSLTU1l6tI1a+obfmEXYIvh\n//fffxEdHW12IS1I4/8u6x0Wnl2I0guY/LrvLF9MOTgJGhdnFqPSgdBXWRgXnJrDDzt+QNvf2oLU\nhHKLyuHkY9MqpOYiBUqlEgsUCmT36WPyfWf+uQKnKr+DCPAv/lbcGf73jX+pUuzjt283++XcenkL\nFZcwzfbBewYjM8d+Qa4//vgD6xQKvCXCqvh48DyPI0cARY9wi0VamZmZOKDtDTqSWPIuPDwce/fu\nxZEjRwqsQEvwmHtt74XY47HwmekDUhPKLy6PVZdXWdSysQV3X99F3ZV1QWpC0zVN813Vaoikd0lY\neHYhqi+rbmT4C88sjK03t9q82Pwv8g7PUp8h5lgMAucGiovqge+0rZ90Ese6YbQ+O/ugzMIyerLX\nPf/oaXYhNuwHIJPJ4O7uLnrLRASVSoVZs2aZJANs3ZqX9vDzY6QkHx/2s4cHsGSJSV9JH5mZyHFw\nwK9KpdkGO1m5WYg+Fg1FrAIOag9Iq65EIW8eFhp8GSEpKUnUsQkODkWLFqfF9hblygGzZrFGNzJZ\n3gLQrJnpIrfMzExs2LABTZs2FZPmTZo0QZ06dT6Jk/Uw+SGG7x8O16mubGFfVh3rr63P8967dGGx\nf50ksu646PlHTwTPC4YkWgK3qW74fvv3CJzDxlXfnX2RnKEfqjEVWnVzc0OlSpVARAiRSrF92zaT\n8+XFu5co0XUBiHLBSTSY98sbUCF6h/+08a9cmcX7rXyRmTmZGLZvGEhNCFscJtLx7MFrbWu4r4gV\nf6jV6WaLtG7cuIGhQ4fC08MDR4nwUirFzOhoowrXjw2VmPOYJdESHHp4qEC9dA3POhs5xznDaYoT\n5v89X78ZuI1IzkjGiksr0GRNEzGRGLY4DNWWVgOn5qCarAKn5kSGRN2VdXHwwUGr91KQxl93kgte\nftvf2kIaLRUXwC03t7CJfvMmmwKW+glozxmxLULMCagmqzDr1CxjYgDMj4sLFy6gWrVq4r35+/tj\n3759Ru+/cYNtiHW95m7drLalFvHq1Ssc8PLCCyJIrTzPWy9viY6By6AvQZ730aWLbeyjJ09YC01f\n310gCgARh9DQwThyJE0MUQlSF0OG5Cm2cxyThzh/nseFCxcwcOBAuLu7g4gQFBQEtVotFgEWVD4K\nYJ47p+YQPDcYnJqDLEaGrlu74sxTEzpVmzaxi7XyOTde3EDL9S1BakLQ3CBRZdVnpg82Xt+oN+5N\njQue57F71CiU1X4/9evXxzkTfUiyc7PRZsp8kCIFRBqQcxHgP238q1RhRGdXV5vqw/fe24vCMwtD\nNVmFRecW4Xnqc9sTjJmZ4J2ccKlGDchkMhQtWhQHDhwQt8WjRo3CkiVLUKtWLTFGG9uwIUAEzbx5\n1s9vB1I/pGLLzS34Zss3eh6lNFqKVutb5SvHYSv+ffuvmEOovaK2VbE0gG2N119bj9YbWkMew8rt\nS8wvgQmHJ4gLsa5XNHD3QLT9rS0Wnl2IgDmsSK7W8lrYd3+f2UWgICf5gN0DwKk5VFtaTfTyC88s\njDGHxuDBmwf6B/M8EBICNGli07mvJV1DnRV5neX8Z/tj//39Nl+bUPzn7+8vGuR69eqJlagCkpPZ\ntCBiO4F+/RjhzBweP36M+fPno2HDhpBIJOigXTUaGhj/cuXK4alBJZaG1+CX87/AdaorZGoVJPWn\norBfNiIijMM0+/ezhahZszzl0i+/BJYuTcOAAT+C4zgEBARg586d6NsXcHXl0a3bMri7u6Nbt2Vw\nceERFpYNleqDdmHbD4WiCbp1647Dhw+bTCJ/jJP1IeeDHgvO5iS+tgocw4bZ9DmHHh4SIxRhi8LE\n3gIt1rWwvtNOTUWOXI5fmjQRq5O7detmVAWvmqwCDQ0COSWCqArwnzf++xg1DQLX2wqS3iWJxito\nbhA4NWd7grFTJ8DXF+f+/lucfML2XHgFBgZi9uzZePXyJVCrFqtayYf2uy54nsedV3cw+/RsfPnr\nl2LM0H2aO0rMLwFOzUEZq8xXslTE6dOMGmnAjTd3Pb9e+RUe0zygiFVg8vHJyM7N1vOYM3Myse3W\nNnyz5Rs4TGY9fQPmBGDEgRE4//y8zbuSDzkfsOT8EtEIV19WHbvvmi7E+ZhJruE1Zie5PEZuMRmH\nsWPtboay794+BM0NEj+j7sq6uP/mvs3vz87Oxty5c8WwDMdx6N+/v0gRnj2bGdcvvmDTQwid1KzJ\nZPszMoBbt25hypQpYoU2ESE0NBQTJkxAu6ZNkU6EBSZ2UzKZDF27djXyMJ+lPkP4xnC2sxleEVTk\nPJRKYOWWBFReUB8tOiWKBr9oUWDSJFaopYszZ84gNDQURITixX+Bk9PXei0xpdIm4LgxIHJFQMAC\nuLikgwioXZuZgILY7P6T/A8WnVuE1htaw2mKkxifF3aqDpMtkDt00aoVo4nbeFG5mlysvrwa/rP9\nGQ17cQU4THaA4xRHzDo1CzmaHPOMuJYtgWLFkJqSgsjISKhUKiiVSowePRpvtboZCWkJaKyeDHJ8\n9Zl4/lpqGnr2tOkBA+bDJVbpeBs2sNs9dUqMVRq+unfvzo7ds4cdGx9v8ZTmvswPOR9w8MFBDN03\nFCV/zms8H7ooFGMOjcGJf08gR5Nj5DHrVi/ajNOn81otOTjYtAAAbCEVukFVXFIR7Te2B6fmELIg\nBC5xLiL7ZeDugTjx7wm7abe6yMrNwrKLy8RkauX4yth+e3u+Q1vCgrr43GJ03NwRXtO99JgYQmjG\npkl+9iz7rtfYp/Gfq8nF4nOLxWcliZag3y7Wr/lywmW4TXXD1aSrFs+RkpKCQYMGidWfTk5OaN/+\nODhOg6CgMdp2nmPBcTzatuURHJyh3Q28BdEcEIWgRo0amDZtmh4j6vjx49ipUCCBCJzOTmrjxo0Y\nPnw4XFxcQESoXbs2tmzZghydmNLvt36H3yw/cGoJqNlPoK97gyZJQK0GoFEjRkyxVE+VlZWF2NhY\no4I3XQdLqMTPyAAWLswTcqvwf9o77/Amqy+Of2/STctoC6WUvRFayqYoFHCxZAmyKYICTsbPgSCI\nAiKKA5SNyFSWDBVQkLKHUKCUvXcHhULpHsn5/XGSJm2TNknfjJb38zzvk/WOk5s359577hlB/Dc1\n1cRFRJSamUo7ruygMTvG5Iy4MZWLu7z919v0x8U/aMTWEaT4XEFu091MH2Qt1ZSDPXHCdGGIKCUz\nhabvm06eX3qS0xdOVOOHGoSpoCYLm1CX1V1y1o1ysWQJX0szvbtz5w6FhYWREIJ8fHxo7ty5tHNn\nFrl5PSE0ak8QICr2yp+IFb8B1zRjRD+JpoEbB+Z0Ato/u8sXLtRrbS9ad3ad4cIOjx+za+kHHxRs\nY1arObNUjRqFJmHXd2+MfhJNS08spV5re5Hnl545Cqjz6s4079g8yRZa8zF2LOUYh/WCo0xFa8rJ\nu7lMcykwZa4lZGZn0rKTy6jWnFo5nc7v53/P6VgKihW4+egmLTu5jAZvGkyVvq2UI2fl7ypT2OYw\nWhG5ggb/Ptj8P7lKxcXCLYzRSMpIonF/j8tZU/CY4UEVv6mY09mbwo0bN+ill17S3IefEPB8nnvz\nBXJyWqcZQXegChXCSanMJoCoQwc2Uef9+6Qv4zq4y1xcaGFYWK6ZVGJiIv3www9Uo0aNHFv7t99+\nS481YaWP0h7lfB9TvLjUajVdvnyZlixZQkOGDKFq1aoZ/H/x4nD1fMdnZhKtWEHUoAHfxrVq8bhr\nxgw2PenfF7t3q+mDKfdpztE51Gl1pxw94DbdjTqt7kRzjs6hSw8u5RpYWDTI0kaBawI7zSUmKSYn\nHYShdszVlnFxfK0pU3Kd4+TJk9SxY0cCQGXKTCeF8/Pk5MIzKZJABwsigj1o3rw5RUREANu2Ad26\nAdu3A507m3TsW3+9hcUnF8NF6YJMVSZ61OuBqmWqYt25dYhNjkUp51LoXq87+jfqj5drvQxXJ1c+\nsHNn4MoVDGndGqvXrMl33sGDB2NV795A797A8uVAWJjB67vPcEd6drrBzyqXroxudbqha92u6Fij\nIzycPUz6ThYTGgrs38/P3dyA8HAgJMTkw2OSYvD+jvex9dJWZKmz4O7kjt4NemP2S7NR0bOiVUTO\nVmfj1zO/Yvr+6biScAWBFQIxud1khN8Ix+KTizGq2Sh8FvoZwm+E83YzHNcfXQcAlPcoj441OuZs\ntcrVghACANB7XW/4e/pjZLORWHxiMWKSY7Cp36bCBXr3XWDZMuDBA8DDst/r3pN7qPx9ZaOf02eF\n/8+OHj2Ktm3bIjs7O99nHh4eWLBgAbp16wZvb2/ExQG//AIsWgTcvAlUqACMGAG8+SawYQPQQnEC\n7T9sDgIg3N2w9+sIHE9tiI8+0p1TpVLhjz/+wPfff48DBw7A09MTw4cPx/vvvw+PCh7osmQQIhP3\nAQo1AMBZuOHVZ3qizzN9UDmjMiIOR2D//v3Yv38/YmNjAQDly5dHaGgowsPDkZCQkO97ODs7Y9y4\ncejatSvatGkDJyennM/UamDrVuDLL4GICMDHB0hPB9qM+wH/Oo1HtUdhuL10NtR9+gA19qKuT110\nrt0ZnWp3Qmi1ULg7uxfaxmbRvj3fE2fP8usjR4C9e/l9E/9j5+6fQ9DCIKhJne8zV6Ur0j9N113r\n4UPgzJlc+xARtm/fjoEDB+LJkyf67wvzv1AepOhBLNlyRv7p6by69frrJveqxnrybFU2hV8Pp5F/\njCTvWd45tvXhW4bTzqs7KXvBfCKAji9bRj4+PuTm60YYBnLzdeMFxj17OD1h3bo5c8+kjCQ6cucI\nLYpYRO9se4faLmub4yKm76HTeEHjnKhbm3HkCA+VtOGeZrShPlofZrNGzBKQrcqmNVFrjEbWYiqo\nzMwy1OO3HjTn6Bw6E3fGOu3777/cjps2Fek0p6JP5aQDyHV/TFVQw3kN6cOdH1LEvYgCTWjG/Nu9\nvLxo7dq1+bzOVCpeOuvRgwePQhC1bElU2jWddqEjEUC7RAfy9cjv4aZPREQEDR48mJycnEgIQY0a\nvUfoOowwBQRNlT28V50UkzRrK5NBGAIq83IZ6hbWjRYtWkQXLlzI+X2MxTzou8KWLVuW+vXrR6tW\nraL4+HhKzUylE9EnaPmpFdT3q0WEGv9qJrUqQqWjBJdEQs8hpJzqbHEdBrPQiwKn1au5cc00rxLx\nzKXS7Eq57gnnL5xp0O+DaPXp1XQn8U7uaxlAW39Du1GJGPkDwNChwF9/AXFxgLOzJOfPUmXh3+v/\nYu25tdh8YTOSMpPwjMoHZ6Y/xJ2xw+EzbQ5CZ4XipOIkmqqbYt/H+5D821JUfHMcNk5+Fb82VCMq\nLgrXHl3LOaeXixcC/QIRVCEI5+LP4eDtg3BVuiJTnYlRzUZhftf5kshuEkRAhw7AxYvA1atA9+5A\ndDRw4QIgzBsUWDxiloi7iXfR//f+OHznMAgEhVAgqEIQZr4wEy/WfBFKhdK6AmRlARUrAl27AitX\nFulUDec1xPkH53Neuzu5QyEUSMlKyXnPRemCBr4N0K1uNwwMHIgGvg1yZi81atTAzZs3AU8AfQBs\nBJAMCCGg/a/WrVsX7du3R2hoKEJDQxEQEACVWoX/zkdj4ZIsrPrFFUgKgIAKApFQoh4qNv8McR03\nIv2rWwXeHtHR0Zg3bx5mzSKoXl0CJD8ATgBoBsDTF85/vInnh50C6gJXxVVcTbwKAKjjXQdd63RF\n17pd0a5aOxw9dBS9evVCgjoB6AlgM+Ct9MbmzZvRMLAhVm1eha1/bsXxfceR8igFEAACANQFUAdw\nCXBBHZ86eHC+FuKWzQLS6ufI6OmlRvNmCjRvjpytZk2zb/vCuXULqF4dmDWLZ4aXLvH7SiUwbRrw\nyScmn6rGnBq4+fgmnBXOOTNsF6ULEjMS+XPPKgg9cAehLfsi9K1ZqF62es49AQBDhgzB6tWr+b5w\nBehB0Uf+jqH8//gD6NED+Ptv4OWXJb9WenY6dlzZgd/O/oaxH/wO90w1mo7OvY9SBZybD2QqgSZv\nCdT2rYsgv6BcW7Uy1YpuYpCKv/9mM9ZPPwHvvAMsXgyMGgWcOgUEB9tODonIa8qzeWc6bBjbHO7f\nL9IApNK3leDt7o0poVPwxb4vkJCWgHvj7+Hao2vYcnELtlzcgsjYyFydgZuTGxpVaIRX6r4C1XEV\npn00DdSVgKYATgJim8B3332HZq2aYes/W7F/336ciTiD9GQ2GTj7OiO7ajaoKgHVASQogbXdgMxv\nANTJJZ/C4xG8q8WgZr00NA1yxfOt/BDawhfly+fWJcHBv+L06SWA5169Tqg9mjQZhZMn++fsd+PR\nDWy7sg3brmzDnht7kKHKgJeLF1KyUqDOUgP3AVQEEA2gAiBcBJwUTshSZ7E8pECV1CrwuOGBxDOJ\niL4UDQAICAhAkyZNsGNnOlSZvwJYBOBtKJyXoUe3AYiODkBkJJCRwXKULYtcnUHz5kDVqtyfv/AC\nMH687rt99x3w779saS6UZs2AtDQeVGlxdwd27zbLvGpIX2zouwFRcVHYd2sf9t3ah/1RfyLBRQUA\nqFK6CkKrhyK0Gm/RZ6Px6quv4vGzj6E6rgJFlxTln57ORst+/YAlS6x63fRZX8JtwiSETPTDUZc4\nAIBCKPDR5QqYuSYWV5d+jUpD37G+rb4oqNVA06ZAUhLflC4ubJv09wf+9z/gq6/sLaHZ2L0z3boV\n6NkT2LWLtYUVISLceHQDGy9sxJaLWxAVF5WrMzAJNVAqoRTKxpYF3SQkXEpAehJ3Bh4eHkhNbQl4\n/gj06QZs3AYkL0OlehXgUrkJYq57IyO6NpBRNud0LmUSULHGQ9RroEJIU09sW7cbJyK6ApX6Agl7\nAe/2QPR6PP/CYqzZ/Aaik6IRkxzDj0n8eOfJHVx+eBnRSdFI2fM2EHAcqLFXJ/ON9sC9lugx4hKa\nVGyCNlXa4Nmqz+b6r8XGxmLHjh3Ytm0bNm16CUSvAZ5dgD5HgI0hQPJ2+PqG4+efneDvXxUpKTVw\n+XJpnDghEBEBREUB2iUTX19eN7h0ieDrOw0PHnyBypUn4N69aZg9W+TqEIwybRq+npIEd08nvPe+\n4AWJefPwXfrbJncgXbqY1gGpZ0zH+bmTsW/1dOx7fBr7bu3D/ZT7+U+4CCVI+QPAoEHAP/8AsbGA\n3iKQ5Fy/DtSqhY0jQtCv6n9wUbqAMjJwd7EnfAPq8EqT5PNHifntN2DgQGDNGn7U0qULcP48cOOG\n438HRyMtjbXFsGHAvHk2v/ytx7ew5swarD2zFmfiz+T7XCmUKOtWFuU9yqNy6cqo5V0L1ctWh7+n\nP/y9/OHn4YfHdx8j8nAkZsw4jPj4n4CujYFmMcAJf2DbaTg5DUK9etHw9/eHd3kfZCh9EJ/sh7hH\nvoh/4IekB1VAjxoAmV66C3veBfr0BTauA1JuArWuAuUfAW6PAbdHgDs/L11GDT9fV1Sq4I4qfp5I\nvhqIrdMHgp7pDgQdAaJCgHNbgddey9UhOCucUbl0ZVQtUzXfNrLXY9w5+zJQqysQegTYFwJc2w5g\nHQDd1L1UqVKoWrUqqlatCj+/mhAiBE+eNML9+wE4d+4aHj/eA3j2Bfr0BDZuBpJXwcmpB/z8msPH\nByhfHvDz4/Fn+fJ8G2gfK21fgttf/You2IZy9ccgJmQp/He8hti4tSZ3IN99B3zwAaFi/VGIab0E\n/kdHIfbigvzHX7wINGiQM5snIlx6eAn7bu7DzGluuOX+KxC/Ezhdksw+ALB5M3vZ2GDkhcaNcT7j\nHuZ92w8jm43E+eljMGDePrM8juxGZibfIF5ewMmTgEKh+2zlSvZQOnIEaN3afjIWV159ldtuwwb2\noDLDq0NK8q4bVChVAQMaDUBscixikmMQkxSDmOQYJGcm5zvWWeGMLFUW29DzolagflxdpCamIikh\nCU8SnkCVrgKywFs2PzqjGrKzaoPS6gEd7wFN/gRO9gO2fQSh8IVC+ECVXbBnjbOzCllZj4Guk4Bm\ni4ETbwLbR6J5iyoo7eOKLKQgA0lIpydIo8dIUT1CkioByaoEkCIdcMoAEjOBk9WATmeBZsuAE8OB\nHR2BOonwK10XWUmlkJnigexUD2SneUKV4QlSeRoWqOvbQLNFwIlRwDZj5kQCoNY8AtpGDEY4ouAE\nddffgGZLgRNvANu6wNMzEF5eNeDsLODiwtZCFxd+rr/du/cfzp7dC3S9ovkerwPbqqBt21A0adIB\nSiVyNqelC6HwKgXlsCH82onf37r1KA4dqgd0fQ44eb6EjfzT0ri7HTwYWLjQuhf//HPeYmKAMmWA\nOnWAKlWAQ4ccf8Q8fz7b+A11VImJPHx5+23g++/tI19xZs0avv+UmgVmFxezbbtSYGjdIPp/0fn2\nS85MRkxSTL5O4b/L/2HfjX2gUsT6S6PTFEoBNST4v6v1NlIAagGoFQApAbVmK8UGLGEAACAASURB\nVPPEcAdEAoity/tCkfuRFJrnmq3KIUBhQF4SQMwzgCITEFmAIgsQ2YAiGxAqflSoAKEGSmUYkQNA\nspvuWlrZSQGQk0YeJ0DtBPhdB4QROe60Bl9A8GtDj9V07rK521EBXNcf6OYRVF+/19qpO0eJM/sA\nQP/+7KceHW1d009UFNC4MS+SpqUBY8bwn7xjR+tdUwqSk4HatYF69djf2FBH1asX8N9/wJ07OiUm\nYxqPHwPe3uxJBVjk1eEovLHlDSyLXAZSEYQSGBEBLBn+O7J6vIK07DSkZaUhLTsNqVmpOc/TsjSv\ns9Mw5euVuOIWA1Q8DSgJUAkgvgGqZJXFgNeeQ0ZmBjKyMpCemY7MrExkZGUgMzsTmVmZyMzOxOkL\np5HgmgKU0ShitQJIcoJnmgv8/fxBaoIaahAR1JT3kbdHSY+RWUoFeGSz4lMrgBQl3FOcUKl8JTgp\nneCkcIKz0hlKpRIKhQJKBT8KhYAQAocP3Ya6gjNQ7hqgBKAC8KgmRHwmGjQpjWxVNlRqFbJV2Tmb\nSqWCKjsbqiePoRYKpGaqofZ2ATxzy4HHWXByc+LulABAQOd+L0CaToGy1UBZAO5ZuuPTXIAnAhDG\n9JzI81wFlE4H3FTAEpJE+VtRw1pA377AunXAgQPsxmgtAgPZN2zNGraztW/v+IofAH74gd1ht2wx\nPkPp358/P3iQA8BkzEOhAFTscQGlku+NYkhCRgLeavEWL6BHLETM9WXAb7/BuXdvOCudUdq1dIHH\n7/VoiZv3n0eWP7FJSElwjk1FF78/MeulmoVe/8UXv8S/rpeBZis0x6uBywPROqMOdi2YaNJ3MHiO\ni0PxrBnneGZhJC7U6gp4Q3MOADfTUf/aNpxbW4BX3JdfAt9OAg7sg3f35XjUJjufHOUOK5GQsLRQ\nGby938h//Pn+ho8nYvfS4GB2QtDg7x+M2Gb3gWZxusFJUZEiWMCSLSfIS5+UFC5s+pYNgoz+9z9d\nWoQFC6x/vaISH8/BcD17FrxfcjK34ejRtpGrJPHRR7p7AuB0IGYE8zg0777LFVWePDH5kO5rulPT\nyU3Jq7YXNZvcjLqv6V74QRpGjbpGToP8SdldSfADKbsryWmQP40aZXpwlhTnICLym+VH7iPcCf4g\n9xHu5DfLr+ADtP81Te3vBg1OkeI1X1J2AcvRBYR+vtQgwLSssw0anCL09yV05ePRFYT+vtSgwSnD\nB4wZw1lFk5Jy3goJ2U14rQuhayWCkzRBXo6l/ImI+vThChYFZY+SgoULdX9yMyP27ML48RxdaEoB\n1H79iHx9zcuQ9bRz4wb/4bSl9QCO6DQzV5LDcvAgf6dVq2x2SSnKg0pxDrMZN47/a2fP5rz1eOBA\nmghQWYAmAvSoVKnCB2J6xMfHU5s2bUgIQc8++yzFxxdQmW/vXv6t1q/PeWvWLKLNmx9TmzZtJIvw\nNW0noBOASwCuAphQwH4twD4DfQo7p1Hlry2ksHevaa1qKTNm6JKSW5AQzabcusWKydT0DZs38/f6\n5x/rylWSGDiQR8ZbtujKadniPrQVKhWnz+za1d6SODY3b3KW3OHDc79/4EDuweJrr/F+mpTLkpKd\nzZXEBgww+DEkKuOoMGINykEIoQQwD0BnAM8AGCCEeMbIfrMA7CySHapLF05QtnFjkU5TKB068HWU\nSvbqcGTb7tSpuR8Lo1MnoHRpYO1aa0lUsjh+HPj1Vw6Q69ED+Owz3WcGkpMVSxQKDqL85x9OICZj\nmClTuK3y/teee45jkYRgV+Dx49ntevNm6WVQKvk+/OsvXQizFSjU20cIEQJgKhG9rHn9CQAQ0cw8\n+40FL2e0APAXERWovQ15+2RlZeHu3btIv32bGzYgwLqulxkZHF3s5ga4ulrvOkUhK4u9n0qXBsqV\ng5ubGypXrgznwlIQDBvGC79xcY773RwBIu74tTmSvLzYA6xiRb4Hu3QBfv/d3lJKw6lTHBm+eDGn\n/5TJTVQUL7R++CHn88nL+fNAw4YctTV2LHve1aoF7CzaeNcg27dzbopt2/ge1EMIcYKImhf5GoVN\nDcCZPZbqvR4C4Kc8+wQA2AdAAWA5LDT7XL9+neLj40kdH090/LhZi1MllitXiE6eJMrMJLVaTfHx\n8XQ9bwklQ2irpG3dan0ZizNbtpDBRf833uAFX2dns6p8OTTaspUdOthbEsekSxeuLVLQ7928OZdY\nIyKaNIlNhLGx0suSnk7k5cX3YR5gK7OPifwA4GMiA0mr9RBCjBRCRAghIuLj4/N9np6eDh8fH4hy\n5XjE/+iRROIVU5KT2ffczw9wdoYQAj4+PkhPN1xLIBfPP8+JTWTTj3GysoCPPgLq1wfeeCP3Z0OH\n8udZWcD69faRT2qEAAYM4BiRmBh7S+NY7N3Lo+2JE4Fy5YzvN3QoEBnJs4QBAzjP1oYN0svj6sp1\nTrZs0bkeS4wpyv8egCp6rytr3tOnOYC1Qoib4JnCfCFEz7wnIqLFRNSciJqXL1/e4MWEEGzzKlOG\nlX8hZqkSCxFw9y7HjPv55bwtTDWDOTtzuoI//gBSU60kZDFn8WLg8mXgm2/yBxU+9xzHgpQqBaxe\nbR/5rEH//nxvlZQOTQqIgI8/BipX5sI+BTFgAP+3VqxgE1BgIOfasga9enHCxoMHrXJ6U5T/cQB1\nhBA1hBAuAPoD+EN/ByKqQUTViag6OPnr20S0pUiSlSvHo64UM7MdlhSePOGRv7+/5ZG6/ftz+23b\nJq1shjhyBJg5kx+LA4mJvKjXoQPbVvMiBI/yUlP5z3fjhs1FtAr167NdW54R6ti0CTh2jFO+uBdS\nDczXl++XNWs4feiAAcDhw1xOTWo6d+YZgDUWlWGC8ieibADvAvgHwAUA64nonBBitBBidMFHF4Ey\nZZ5e04921O/qyjebpbRrxwuX1v6jHznCi6YTJ3KkdHHoAL76ikdVs2cbdyoYMkQ38yxpo/+jR0tO\nh1YUsrL4vn3mGe7sTSEsjB0pdu7ktgSs8x/z9AReeok7JytYQEyy+RPRdiKqS0S1iGiG5r2FRJQv\nAxsRDaNCPH1MwsmJPVxsbPpRKpUIDg5Go0aN0LdvX6RqTCaenkayBQK4efMmfv31V+mESEhgj5NK\nlXJn7TQXpZJT6G7bxjMJa7FzJ3vGAOxBtXev9a4lBbdvc+K7IUPY+8UYNWsCbdtyXd9Vq0qOCbJf\nP35ct86+cjgCy5ax6W/mTNPziXXpwutpK1YANWpwBl1rmX569+Y8XSdOSH5qqRZ8rUO5cqxUCrJZ\nS2xucHd3R2RkJM6ePQsXFxcsNCHDqKTKX61m1053d04yVlT69WOFrJcnRHJu3cr92tFzCk2axI/T\npxe+r9b0c+UKxwOUBKpX50yl1lJYxYWUFDb9Pfss8Morph/n4sLmnq1beXA6YAAvAJ8/X/ix5vLK\nKzyIs4Lpx7ESu+kzdiz7JScnc2Mb8lVPTORGV6t5hBwUxOYiYwQHc3I0E2nbti2ioqIK3W/ChAm4\ncOECgoODERYWhnHjxpl8jXw8eMDKuk4daWIcWrfmenbr1vFIV2quXeMAqRdf5Fp6GzZYNTClyJw4\nwSacCRO4XQqjb1/gvfd4ELJqFdCypfVltAUDBgDvv88K65l8MZtPB3PmcPGojRvN/6+FhXHRlfXr\neXY9bhx3ptOmSSujjw8PpjZtAmbMkPTUjj3yF4KnYtq6bHlJTGTFD/BjYqJkl87OzsaOHTsQGBhY\n6L5fffUV2rZti8jIyKIpfpWKXfC8vNjkJQX6kZ3WiFYdN45/o19+4Wlw2bJWL8VpMUTABx/wOsqE\nCaYdU6YMl3d0cuI/d1aWdWW0FX378r3xtC78PnjAgVw9evDI31yaNeNOc+VKXlfr0IHvD2uYBnv3\n5iBE/TrCUiBFsIAlm6Egr/Pnz+cPdtAGfCUn5//s8GHOs6FUSpacTaFQUOPGjalx48b07rvvUkZG\nBhERlSpVyugxe/bsoa5S5Ey5d4+/q142P0MYbKeCOHGCA5mWLCmCcAb46y8+76xZuvfee49znjx4\nIO21pODPP1nen34y7zhtwBzA5ygpPP88UZ06HPz1tKFN3mZKokRjzJrF98Tly0RLl/LzY8ekk1HL\n3bt87hkziEi6IC/HV/5ZWUQREUR37hhumMOHOSmbRFk5jSl5qyv/zExW0leuFLqr2cpfrSaqXZv/\n7FKRlkZUqxZRvXpEmg6SiIiiovi2+u476a4lBVlZRPXrc4RrZqb5x1asyNG+r71mHfnswZIl/FtF\nRNhbEtty4wYPUEaMKNp57t7lDuTTTzkq2NmZOxVr0KoVkUZnSqX8HdvsA/B028vLuNdPSAhXWrJD\nrVUtXl5eSEpKKtpJYmPZdBUQII1Q+gjBLml79rCLmhR8+y3b++fO5TUZLYGBQKtWbPpxJO+YpUt5\n6vz11xykYw5OTlzeUaXiRT4JzYt2pXdvbounzfRjLHmbuQQEcL3xlSvZPNi5M6+tWSMit3dvXq/K\n61xRBBxf+QPs9ZORwe6PDkhQUBCUSiUaN26M7y2pnZuRAdy/z7bowoJMLKV/f+5cpMiWevs2Lz71\n7s1+yHkZOZLtk4cPF/1aUvDkCWfqbNcO6N7dsnOEhXH7ZWSUnERv3t7Ayy+z8lcXmJml5HD6NC/4\njxnDEb1FJSyM/w/79/MienQ0VyKUml69+HFL0WJncyHF9MGSzWSzDxFP048fN276Ke7cuMFT7/R0\nk3Y32+yjpVEjouees+xYffr04TWWmzcNf56czEmpwsKKfi0pmDRJGntskyZcV6EkJUZbs4bb5sAB\ne0tiGzp3Ljx5mzmkpPC9PmyYroreyJHSnDsvjRoRtWv3FJl9AJ6a2iHgyyY8fMieB+XKWT/1cv/+\nnKrgzh3Lz/Hvvzx7mDgRqFbN8D6lSgEDB7Ib3OPHll9LCu7eZRPVwIFAixZFO1dYGI/89+wpWhs6\nEt2782zzafD537MH2LGj8ORt5uDhwa6e2hl1z578XBv0KCW9e0ua56d4KH/AIUw/Z86cQXBwcK6t\nVatWlp8wOVkXYv/oEb+2JtrITkuTemVmss97zZrsMlkQb77Jv9WaNZZdSyo+/ZQHDFL4SA8YoIsC\ntff3kgpPTw4k2rDBuEt1SYDMSN5mLkOH8n930ya+RxISgF27pL0GwKYftRoBQEUpTld8lH/Zsvxo\nx1w/gYGBiIyMzLX9999/lp9Qf/GVCCjqonFh1K7N/smWLvDNncuLpnPmcAGcgmjWTFc4xF6ztVOn\neDFuzBiOai0qFSrwop6LC5+3pMxC+/cH4uOB8HB7S2I9vvySI7SHDpV+Xe255zjNw8qVvAZWrpx1\nZlKpqYAQqMj1U4pM8VH+zs4Fe/0UNzIzc3uNKBT8/axN//5ARAR76phDdDRnPezalfOMm8Kbb3IE\ntj3SIpAmoMvbm73BpGLoUP7tLlzgvO4lgc6dS3bZz717gcmT+fn330ufeFCh4Pti924e0PXpwwuz\nUqdS37dP0sqGxUf5A9yjpqfzVty5fZsVVM2a7DJWty5Pwa3Na6/xo7lJvT76iJXenDmmHzNwINtE\n7RHxu2MHj2SnTNHNGqXglVf4fEJwuoeSgJsbmxQ2bXLs1ByW8tlnugFjZqZ1Eg8OHcrXWL2aTT8p\nKcCff0p7jfbtAVdXECDJ6Lf4KX+g+Kd5TkjghdBKlXhk6u9vG8UPcD6bNm3MG+Xt38827o8+4pql\nplK6NK8z/Pab9U1a+mRncx3W2rWB0RJnHXd15T+3QqHL6V4SGDCAZ6J//21vSaTl2DF2vVQqeXNx\nYSUqNTVrsvlnxQrOBOvvL73pJyQE2L0bcUC0FKcrXsrf2ZmVpDVy1GgwltLZVLZs2YLzBWX3y87m\nUb+HB+cEsQf9+wNnzpiWhTA7mxfIqla1zHwyciSPgmxpUli2jL/brFm5A9CkYuhQDuS5f5+n+iWB\njh05zqQkef2kpwOvv84z6x07OOna7t3WCwgNCwMuXQJOnuRBz44d0nu7hYTgHhArxamKl/IHeKSc\nng6kpSElJQUTJ05EuXLlMGnSJLMVtSEsSemsT6HK/84dVhzVq0tqvzMLbVIvU0w/CxZwR/H999xh\nmUurVkCjRrzwawv+/Rf43/840lgbGCM1rVpx1lWlsuSYfpyd+b7488+SUz3v8895ELBkCWedtXYm\ngL592YS2YgXPpDIz2ZTmqEgRLGDJZlaQlz4ZGUTHj9O+338nHx8f8vDwIADk7u5OPj4+tG/fPlPC\nJYyin8NnwYIF9NZbbxER0YoVKygwMJCCgoJo8ODBBo89dOgQlStXjqpXr06NGzemq1ev5t7h8WMO\nVrt7t0gyWhzkpU/HjpznpqCkXnFxRGXKEL34YtGSf82Zw4FEp05Zfg5TOHyYyMmJr+XqKlm+J4NM\nn87XcXMrNBFfsWHfPv5Ov/5qb0mKzrFjnHdn+HDbXnfAAKJy5XS5r154QfJLQKIgL4fN5z927FhE\nGvOmSE3FhWvX8FDP/JOWloa0tDT07dsXDRo0MHhYcHAwfjAxn782pXOnTp1w7tw5TJ8+HYcPH4av\nry8SjJid2rRpg+7du6Nbt27o06dP7g9VKs7L4ebG9kB7078/m2QiI4EmTQzvM2ECjwLnzi3aLGXw\nYF4vWLIEmDfP8vMUxrp1Oht8djYv7FlrpDdkCMcQpKdzoQ1r1EqwNc89xyaS337jkWtxJSMDGDaM\n19S++8621w4L4/bbto3/YzNnct4ue5l4C6D4mX0A08utWUBaWhqCg4PRvHlzVK1aFSNGjEB4eDj6\n9u0LX009XW9LKmzdu8fTwOrVi1aaUSp69+Z2NGaLP3qUc/SPG8dFv4uCtzdPiVevlt79TUtGBvDX\nX/zcmgt7WqpW5RzuTk7s310S0NZ++Pvv4u1U8cUXbO5ZvLjg4k7W4IUXeHC3ciV3oGo1B9A5IlJM\nHyzZLDb7EBE9ekSDO3cmsMtTrs2YScZUDKVunjt3Lk2cONGk48PCwmjDhg2533zyhM09t24VSTYt\nkph9iDjPSbVq+U062dmcPrZSJZZdCrQmhV9+keZ8eRk/ns//zTeSpvgukOXL+ZpCcC2GksDx4/yd\nli61tySWcfw41/d4/XX7yfDhh2x+vH+fKDCQKCRE0tPjqcrtk5e0NLzZsyd8ypSBuyYfjru7O3x8\nfPDmm29KfrmOHTtiw4YNePjwIQAYNfsABtI7q9Vs7nFxsU665qLQvz/LljdK+eefOX3s7NnSBZ61\nbQvUq2cdn//du3l6/9ZbHNhlqxTfvXtztChRyfGSadaM3XmLY8CX1tzj52d7c48+YWFsdvz1Vx79\nHzkC3LxpP3mMIUUPYslWpJF/UhJRRASlHDhAE19/ncqWKUOTJk2ilJQU044vAGNFW5YvX04NGzak\noKAgCisgW+XBgwepQYMGFBwczAu+d+7waCQxsciyaZFs5P/4MS+Mjhmje+/BAyJvb6LQUOkrPH3z\nDY8qz56V7pwPHxIFBHChFgl+f7MZMoRHmkFBtr+2tfj0U14sjYmxtyTmoc3eum2bvSXhmXOTJkTX\nr7NMM2dKdmo8NZW8jJGURHT6NHuQqFSmH2dLkpNZ8d+4IelpJVP+RES9ehH5+7Oph4ho9GhWZlFR\n0l1Dy/37XO1o7FhpzqdWc3ppJyeugmYP/v2Xcko8njljHxmk5uxZ/j4//mhvSUwnIoLv22HD7C0J\nM3cut2FUFFHr1pIODqRS/sXT7ANwsFe1ajy9evDA3tLkR2vucXaWpmiEtejXj4vGHzjAwSmLFnFQ\nlwmF682mfHn2vV+5UpoUHStXcvrcadM4iZw9aN+evUqAkuPz37Ah//7FxZTlKOYeffr31zkDDBzI\nOa7OnbO3VLkovsof4PQBpUrpSiDakBkzZuRL7zxDP21wXBx7tlStalXvpCLTrRsHb/32Gyv98uWL\nXt6uIN58kyO0ixr8cv06y9uuHadysBdKJdt4heA/ekmpiNW/P1dik7BsoNWYPh04e5a9e6TK019U\nypfnJIirV/PakELheJ2pFNMHS7Yim320JCayaSUuzvxjrUVqKk9D8wZ5SYSkZh8iov792WMFYLup\nNVGpiGrUIGrf3vJzZGURtWnDAWjGqonZkgsXdKaf3bvtLY00XLvG32fWLHtLUjAnTrC5Z+hQe0uS\nn02buA23b+dgr5o1JVlHw1Nv9tHi5cUmIDuM/g1CxKMlhYJH/cWB4GBd1sPvvpM+5a0+CgWP/vfu\nBS5ftuwcX33Fo9L5841XE7Ml9esDzZvzdysppp+aNYGWLdnza+ZM694TlpKZyeaeChUAE4M3bUrX\nrhzjovX5v37dPunNjVD8lb8QbHPNzHQM2//9+1zVp0oVtvcXB7KydBG81kp5q8+wYWwuWbrU/GOP\nHWOz1IABbEt1FIYN48HH+vXWC2SzNSEh3EF/+inw/POO1wHMmMF5pxYtchxzjz4uLnyfbtnC7efk\nxEGTDtKOxV/5A7rRf0yMfUf/GRkcyVu6NODjYz85zOX55znthC0iYwGOgHzlFWD5cvNqnSYnA4MG\ncbzE/PlWE88itAt8qanAH3/YWxpp0CbyU6ttMygwh1OnuDrXkCF8LzkqYWHs3LBgAbfj4cMO05GW\nDOWvHf1nZdlv9K819wBsiihixs4vv/xSAqFMRJMn3Oopb/V5800uHWiOohw/niuQrVwpbYEWKfDx\n4cVzhYKzOpYEXnlF56zg5GT9QYGpaM09vr6Oae7Rp3lzoEGD3EFzDtKRlgzlD9h/9P/wIfDkCbt1\naqKOC4OIoDYiq02VP8AK31aRsQDw8stsGjM11fPWrRwd/NFHQGiodWWzFK3pZ+dONv8Vd0JCgH/+\n4f9WzZqcytoR+PJLdp1cvJht6o6MEDz617p922p2bQLFWvnHrYnDkepHsFexF0dqHEXcQdcij/5v\n3ryJRo0a5byePXs2pk6divbt22PMmDE5hV6OHTsGAJg6dSqGDBqEkI4dUefVV7Fky5acY7/55hu0\naNECQUFB+Oyzz3LOX69ePQwdOhSNGjXCnTt38skwYcKEnARzgwYNsvi7ODRKJTBiBLBrF3DjRsH7\nxsYCb7zB2Ue/+MI28llC585se1ar+bs5wNS+yHTsyFldL1xwjJQPkZFs6x882LHNPfoMHsydwKBB\ntp1dF0KxVf5xa+JwaeQlZNzKAAjIuJWBS2PuIG6Pwmqj/9TUVERGRmL+/PkYPnx4zvtRJ08ifMEC\nHDl0CF9Mm4bo6Gjs3LkTV65cwbFjxxAZGYkTJ05g//79AIArV67g7bffxrlz51DNgLfKV199lVNU\nZs2aNZJ/D4dh+HA2k/z8s/F9iLgaU3Iyl020RmUuqXBxYXsuwBlGHcS2W2SGDuUgugkT7LuYrTX3\n+PiYV0va3gQEcLbP8HDg448dQvEDcNx8/lfGXkFyZLLRz58cfQLKyF3HWJ2qxsXJSYheKwDXCMAl\nt7eNZ7An6vxQx2KZBmhynLdr1w5PnjzB47t3gUeP0OPZZ+Fesybc/f3RoUMHHDt2DAcPHsTOnTvR\nRJMrPzk5GVeuXEHVqlVRrVo1tG7d2mI5SgxVqgCdOnHZxalTDQfDzZvHKYbnzWPbqaPj56d7np5u\n3ZoCtkKh4EpuoaHsCvzpp/aRY+ZM4PRp9p5xdHNPXsLCeAZw4IDDmC1NGvkLIToJIS4JIa4KISYY\n+HyQECJKCHFGCHFYCNFYelFzk1fx53pf6WSeF4keTk5Ouezw6XppCIT+Ii4RxLVrQFoav69XgF0I\nASLCJ598gsjISERGRuLq1asYMWIEAKBUqVIWyVYiGTmSZ2rbtuX/7Px5jt7t0oUzdhYHgoJ0z4ls\nn0/eWrRrx5GqX30FREtSP9w8Tp/mSN5Bg4AePWx//aLSqxd7T/3vfw4zGyx05C+EUAKYB+BFAHcB\nHBdC/EFE+oVqbwAIJaJHQojOABYDKNLqUGEj9CPVj7DJJw+u1VzRZHcjLqRcpVLukZgJ+Pn54f79\n+3j48CE8PT3x119/oVOnTgCAdevWoUOHDjh48CDKeHqijEaJb923D588eICUzEzs3bs3x2wzefJk\nDBo0CJ6enrh37x6czfD7d3Z2RlZWllnHFEu6dmXXzyVLcv+pMzL4j+7lxTMDe9U7NpeHD1lWbdDc\npk3A22/bVyap+PprrvH76af8m9iKAwc4B5WXV/Ey9+hz+jTf0ydO8DpKeLjdZ4SmjPxbArhKRNeJ\nKBPAWgC5ul4iOkxE2tI/RwFYPZNZzRk1ofDILb7CQ4GaM2ryTeLlZVHUr7OzM6ZMmYKWLVvixRdf\nRH29KlZubm5o0qQJRo8ejZ8//zzn/aC6ddGhXz+0bt0akydPRqVKlfDSSy9h4MCBCAkJQWBgIPr0\n6ZM7z38hjBw5EkFBQSV3wVeLkxPb9HfsAO7e1b0/eTIv7v38s9kduF1p355jJrQUJZLZ0ahVC3j/\nfY7POHXKNtc8coSVZUwMlxQtrm25d69uQKA1B9qbwvI/AOgDYKne6yEAfipg/w/09ze2SZHbJ3Z1\nLB2udpj2iD10uNphil0dq/tQWz0rNtb4CcwgNDSUjh8/zrk5rl/nc9+5Q5+NH0/fTJ8uyTVMRfLc\nPvZGm0fm88/5dXg45xoaNcq+clnK4cNcOFxbSL5jR+lrI9iLR4+IfH05N5MtvlP37rq8SUolV2kr\njhw+TOTursuhNWOGxaeCI+b2EUJ0ADACwMdGPh8phIgQQkTEx8cX+Xp+g/wQcjME7dXtEXIzBH6D\n9EaIRRj9F8jduzy1r1SJffq9vEz265cxQs2awIsvcrqHhw/Zu6ROHeDbb+0tmWWEhPCMZfhwdj0O\nD2dPpZJA2bLA55/zyHXrVutea9UqDgJUKBzKP94i9AMp69Zl99kCKgLahMJ6BwAhAP7Re/0JgE8M\n7BcE4BqAuqb0OpJl9SwIiUf/FBOjq8Ur0ainZcuW1Lhx41xbVCGFVErcVvQaxAAAFxdJREFUyJ+I\naP16HhFVqMAjvOPH7S1R0UlIIKpYkcjDg0fLDx/aWyJpyMoieuYZotq1iTIyrHONTZv4PujYkWjP\nHtvVZbYFkZFcgKiAioAFAVtV8gIvCl8HUAOAC4DTABrm2acqgKsA2ph6YZsofyKiixe5sbWVqiwl\nPp4V0tWrdp/Cl0jlry3wDnC1r5LyR9em9RWC6I037C2NdOzYwd/r22+lP/c//xC5uHAFrKQk6c/v\nCHz6Kbff33+bfahUyr9Qsw8RZQN4F8A/AC4AWE9E54QQo4UQozW7TQHgA2C+ECJSCBFR9DmJREiR\n8+fxYy7AXLo0UKNG8fE8KU4cOqRrV7XaMRbEpKBXL6BPHzZdLF0KHDxob4mkoVMnTtHxxRfS5tM6\ndAjo2ZNjOrZvz+VCXaL49FP+jiNHAmY4gkiJSTZ/ItpORHWJqBYRzdC8t5CIFmqev0FE5YgoWLM1\nt6bQZqFv+1epzD8+KYmTiZUqxd4OimIbFO3YaL1kirtt1xA//sgDB1dX/rNbGIPicHz7LUde63m+\nFYmTJzmmo0oVzo/kiGmapcLVldeF7twBJk60iwhPhybTjv7NXWROTQWuXuUfqnZtVkwy1sEemUVt\nRcWKHBmbkcE5corrQnZeGjbkzmzBAv5eReHCBZ5JlC0L/PsvF2gp6YSEsOvsTz/ZZUYo2IRke5o3\nb04REbmtQxcuXEADa4XwX77Myjww0DQlnpEBXLzIz+vXdyiPHqu2k4x1IGLltmcP33/nz7OXkzXZ\nu5fzx3foYL3OND6ePbOefdZwlLYp3LgBPPccm/sOHOCB1tNCSgrQqBHPdk+fzh0jYgQhxAkprCtP\nx8gf4NF/drZpo/+sLO4s1Gp2y3IgxS9TTBGCUxC7uPB9+NZbuqAfa7BgASv9SZOsm2CufHm2X2/f\nzqYac4mO5qRn6emc4fVpUvwAm5OXLGF9I5X5zESKrfL/+mseROmzZw+/bxBPT7a7FmL7VyqVCA4M\nRKNevdD3iy+QauYfdP/+/WjatCmcnJywceNGs46VKeFUr87JyVQqVpTr11vnOuvXA++9p3tt7YjS\n997jWcz48dyxmcqDBxzfcf8+J+/TS6X+VPHCCxwT8s03vO5hI4qt8m/RAnjtNV0HsGcPv27RooCD\nChv9q9Vwd3ND5OrVOHviBFw8PLBw4UKz5KpatSqWL1+OgY5UX1bGcXjnHS6KolSy0nz8WLpzE/F6\nQr9+bI93d8+dZ8hauLryqOvcuYLTc+uTmMgeQ9evc/rrAv+4TwHffsvrHCNGsOXBBjhsSuexYzm1\nS0FUqsRmVH9/Tv3RoAHPnIzNnoKDPfHD25rRf/nyuW3/RGx7JGJ3zjJl0LZtW0RFRQEAVq5cidmz\nZ0MIgaCgIKxatcrgNapXrw4AUMheQTKGUCo5KVpwMA9CJk6Uph6xSsXFwX/8Eejbl0tdnjrFi6fr\n1nFGzC5dgMZWSrjbuzdn/pw8mesZF5TNNDWVS16ePs1Rwg6S4tiulC3L90GvXjwDsIEHULHWUOXK\nseK/fZsfTfIMMzT6J+KTPHrEIyUfH2RnZ2PHjh0IDAzEuXPnMH36dISHh+P06dOYU1wzC8o4Bs88\nw0oSYNv8f/8V7Xxpaazwf/yRTS9r1/LCYUgIX2fXLv5z9OhhvsebqQjBHk0PHnCZRWNkZHBHcfgw\n8Ouv3CHJMD17svni88+L7j1lClJEilmySRHhGx7OUfOTJ/NjeLiJB166RHTqlC7q9+7dnERtCoUi\nJ83Cu+++SxkZGTR37lyaOHGiWbKFhYXRhg0bzDrGVEpkhO/TRkYGUcOGRAoFUaNGnDLBEuLjORJW\nCKI5c4zvd+wYkZsbUbt21kvJQMQpC1xcOFlfXrKyiF59lSNbf/7ZejIUZ2Jjiby9iUJCjGYlgCMm\ndrMlWhv/+vUcZLh+fe41gALRH/3HxbHNyNcXCAjIKZ8YGRmJH3/8ES6OXDZQpvji4gL88gs/P3vW\nsjz1164BbdqwfXTjRvYZN0aLFmyP378fGDPGMplNYcYMTtM9IU/NJ7Wa6zD//jvwww+8wCmTHz8/\nvheOHOHqdVak2Cr/48dZ4XfowK87dODXx4+bcLDW8yc6miPsPD2BatWMpm3o2LEjNmzYgIcPHwIA\nEuydjU+mZNCiBS9uAeySeeuW6cceO8ZmnYQEDorr3bvwYwYOZKW8cCGbm6xBQADw0UfAhg26wCUi\n7nBWrOCRmjU7n5LAoEFA587AJ59wWhlrIcX0wZLNZondjHH/Ppt6jh8nOnEiJ4FUqVKlDO6+fPly\natiwIQUFBVFYAdn4jh07RgEBAeTh4UHe3t70zDPPSC66bPYpQaSkEFWtymabzp1NSxq4dSvnhq9Z\nk02Y5pCdTdS1K2eVNNlOaibJyUQBAUTNmxMdPMi5/wGiDz6we1LEYsPt20SenkQvvJCvzWCrrJ7W\n2uyu/KOjdcr/+HF+XUyQlX8JIzyccjKabtpU8L7z5vE6QYsWRHFxll0vMZGoQQMiHx8uTGQNVq7k\n76NQUE4hlkOHrHOtksr8+dx2y5blelsq5V9szT5FxstLl6RNoeDXMjL2oEMH9u8GgFGjDGd5VKvZ\nZPPOO1z3eM8ey/PflC7NRVLUaqB7d+tklaxShd1a9Qsp7dsn/XVKMqNGsfvs+PG8LikxT6/y9/Tk\n1A0BAfxoZurYGTNmIDg4ONc2Y8YMKwkrU+KZPZudDrS+//pkZACDBwOzZnFaiE2bOC1AUahdmxfJ\nLlzgymlSVbtLTeV1jI4dc/teK5UlK1OrLVAoOPVDejrw9tvSB+tJMX2wZLO72acYI7dTCWXLFsop\n/BIRwe89eqSzmc+cKb3NfM4cPvfkyUU/16FDRHXq8PnefZdo926d2ackFeixNbNmcRuuX09EstlH\nRqbk0aMHR3gScQGYDz8EmjThAidr1rDZR+pCQu+9x26X06axh44lpKezh0/btlyrYPduDjj77z+d\nvFlZnABOynraTwvjxwPNmgHvvss1rqVCih7Ekk0e+VuO3E4lmNhY9uTRLgADRD/+aN1rpqcTtWnD\n1z150rxjjx3jxWOAaORIrput5fBhPqdSyRtA1LNn7n1kTENb97dzZwoA7pI88peRKWH4+QGtW+d+\n79w5617T1ZXXEXx8ePYRF1f4MRkZPJIPCQGePOGsnIsW5Xac0C/Qs38/B3f9+Se/f+2a9b5PSaRx\nY2DIEGDHDlQEAqQ4paz8ZWQcjbp1c7+2RdlHPz9OsvbgAfDqqwVf89QpDlCbMYMV0tmznGHRECEh\nHKzUpg0Hd/39NwdXtmjBSedkTKdGDUlPV+yVf0xSDEKXhyI2OVaS8ymVSgQHB6NRo0bo27cvUlNT\nzTo+IyMD/fr1Q+3atdGqVSvctGaEnkzJJCwsdwGhv/+2TZm/pk055cShQ+xSmte7JCuLk461bMle\nSX/+yfuXLWv6NV54gcPwtSl5f/jB+imnSwovvAC4uYEASRqs2Cv/afun4eDtg/hi3xeSnE+b2+fs\n2bNwcXExO5//zz//jHLlyuHq1asYN24cPv74Y0nkknmKCAlhP/4vv+Q0v66u7O/94Ye8uGpN+vXj\nVBNLl+bOLXP2LJujpk7lfc6d47TMllCrFueu6dGD01C//rr1v1dJICQECA9HHBAtxekctobv2L/H\nIjLWeEL/A7cPQE35PQcUQoG2VdsaPCa4YjB+6PRDgXJ5enoiOTkZALBw4UJERUVh/vz5Jufzf/nl\nlzF16lSEhIQgOzsbFStWRHx8PISEXhpyDd+njORk4IMP2KbesCHn6m/a1HrXU6s5V9Bff3H8wY4d\nQHg4++0vWsQeSVJdZ9o07lBatgQ2b+YZgUyBPPU1fFtWaokKHhWgEPwVFEKBCqUqoFVAK0nOb2k+\n/3v37qFKlSoAACcnJ5QpUyYnIZyMjEV4enIyth07uOZEq1asNM0pmWgOCgWwahVQtSqPzHfuZNPM\n8uXSKX7tdT77jBebz50DmjcHjh6V7vwyBeKwlbwKG6EDwFt/vYXFJxfDzckNmapMvNrgVczvWrSq\nSGlpaQgODgYAtG3bFiNGjMCiRYvQt29f+Pr6AgC8vb2LdA0ZGYvo1Ak4c4b9vadMYZv7ihVcwk5q\nvLx44Xf2bN17p09bp/hKr146M1BoKM8uhg2T/joyuSi2I38AiEuJw+hmo3F0xFGMbjZakkXfoubz\nDwgIwJ07dwDw7CExMRE+Pj5FlktGBgDg7c0VsNav5/q3TZvyoqk1gqd69+Y6wEol1x+wZnqGwEBe\nCG7bltcAxo613sxGhpEiWMCSzVGDvAyldD579izVqVOHHjx4QEREDx8+NHr8Tz/9RKNGjSIiot9+\n+4369u0ruYyO0E4yDkBMDFG3bhw81b490Y0b0l/j8GGiL7+0XWqGrCyiMWP4Oz3/PJHmPyejA3JK\nZ+tQ1Hz+aWlp1KdPH6pVqxa1aNGCrhkqZ1dEHKGdZBwEtZpLInp58bZ0acnImb9sGZeDrFmTaNUq\n23ZADo5Uyt9hvX1kjCO3k0w+bt5kc8nevZzyeckSwN/f3lIVjaNH+bskJHCOIDc3jhgOCbG3ZHbl\nqff2kZGR0aN6dVaMP/zAj40aAdOnAzNn8mJqcaR1a2DkSH5OBKSl8ULwzz9zJLJMkZCVv4XI+fxl\nHA6FglMonDrF6RomT+baAO3aAXPnsvIsbnTvzovOCgUXhn/yhAvBV6wIPP88B8FZodDJ04Bs9imG\nyO0kUygzZrDy1/9/u7kBzz0HvPgib40b66rZOTJHjrA5q317ng2cPMmxAb//Dly6xCahNm3YNfXV\nVzk+oQQjldnH4ZR//fr1JY2GLWkQES5evCgrf5mCOXKER8aZmeymOW0acPcusGuXLkuory/vo+0M\nipvSJALOn+dO4Pffgagofr95c11HUKeOfWW0AiVS+d+4cQNeXl7w8fGROwADEBEePnyIpKQk1JA4\nw59MCUR/xKy/SBodzesCu3ZxZk2t2aROHV1H0L49J2wzdg5H5MoV3Yzg+HF+LzCQO4FatYDbt7le\nsqN/j0Iokco/KysLd+/eRbqc5Mkobm5uqFy5Mpydne0tikxJQDt63rWLt337gJQUNgc1aMBmFZWK\nZw9LlwKdO3OgmaMPzm7f1nUEeTOitmoFBAfzInm1arrN379YmMFsqvyFEJ0AzAGgBLCUiL7K87nQ\nfN4FQCqAYUR0sqBzGlL+MjIydiYzk10sd+3i1BGaaPVcuLhwAjZ/f3409rxcOT6XrWYOKSlciCY2\nVvcYGwusXQtcvqzbz92dO6+86dqdnYEqVXSdQt7O4e+/gS1beCah9UKyAzZT/kIIJYDLAF4EcBfA\ncQADiOi83j5dALwHVv6tAMwhogIzrMnKX0bGwdFfN3By4spdnp5sNoqJ4Uft88TE/Mc7O3OKBiIe\nUbduzZ2ChwcrYO2j/nNDj5cvcz3g6tW5Q8mr3LXPNdl4cyEEp8TWtyYIUXANAaWSH1Uq4/t4eLBZ\nTCu/VlZPT3708uLnpUvz8zJlgIgI4MQJ9r7q3Zvbx9VV9+jiwu2svzk7654rFMCSJag/atSTi0Rl\nTPoNC8CUxG4tAVwlousAIIRYC6AHgPN6+/QAsFITfXZUCFFWCOFPRLIPloxMcUVbhtGUkXtKiq5D\n0D5u3gwcOMCfq9UciPboEY+409L4MTXVsrxE3t7szlqxIi/wVqzIm/Y97WP58mz/79BBt/i9ezen\nxk5I4ILo+o/6zx884O/y4AF3LvqdgVZ2Szh1CiggM3BheAKlLT5YD1OUfwAA/bnfXfDovrB9AgDI\nyl9GpjgTEmKauaZUKaB2bd60tG6d2+No48b85yLiCmFpaboOQf/xl1+4foFazSPyDz4AvviCz2fO\nd9izJ38nVrYsULOmaedYvBgYNUr3+rvvuKhNRgZ/v4wM3ZaZybInJfGMKDmZ4xGuX9cdX6kSt012\ntm5TqXSPeZ+rVMCFCxznIBGmmH36AOhERG9oXg8B0IqI3tXb5y8AXxHRQc3r3QA+JqKIPOcaCUBr\nLGsE4KxUX8SK+AIoDuGEspzSUhzkdHgZvYBSnkDFZCA2CUix5Pg6QF0AAgBdAS5bch4TKbA9/QDf\nskC5x8CjODPb3Q/wrQxU076+C9yy9Bw3ATwgKvKKuykj/3sAqui9rqx5z9x9QESLASwGACFEhBSL\nFtZGllNaZDmlozjICMhySo0QQpLFUlP8mo4DqCOEqCGEcAHQH8Afefb5A8BQwbQGkCjb+2VkZGQc\nl0JH/kSULYR4F8A/YFfPZUR0TggxWvP5QgDbwZ4+V8Gunq9bT2QZGRkZmaJiUhlHItoOVvD67y3U\ne04A3jHz2ovN3N9eyHJKiyyndBQHGQFZTqmRRE67RfjKyMjIyNgPx49llpGRkZGRHKsrfyFEJyHE\nJSHEVSHEBAOfCyHEXM3nUUKIptaWyYAMVYQQe4QQ54UQ54QQYwzs014IkSiEiNRsU2wtp0aOm0KI\nMxoZ8q36O0h71tNrp0ghxBMhxNg8+9ilPYUQy4QQ94UQZ/Xe8xZC7BJCXNE8ljNybIH3spVl/EYI\ncVHzm24WQpQ1cmyB94cN5JwqhLin97t2MXKsTdqyADnX6cl4UwgRaeRYW7anQT1ktftTilqQxjbw\nAvE1ADUBuAA4DeCZPPt0AbAD7MfbGsB/1pTJiJz+AJpqnnuB01nklbM9gL9sLZsBWW8C8C3gc7u3\np4F7IBZANUdoTwDtADQFcFbvva8BTNA8nwBglpHvUeC9bGUZXwLgpHk+y5CMptwfNpBzKoAPTLgn\nbNKWxuTM8/m3AKY4QHsa1EPWuj+tPfLPSQ1BRJkAtKkh9MlJDUFERwGUFULYtPgoEcWQJhEdESUB\nuACOUC6O2L098/A8gGtEdMuOMuRARPsBJOR5uweAFZrnKwD0NHCoKfey1WQkop1ElK15eRQcS2NX\njLSlKdisLYGC5RRCCACvAfjNWtc3lQL0kFXuT2srf2NpH8zdx2YIIaoDaALgPwMft9FMu3cIIRra\nVDAdBOBfIcQJwRHTeXGo9gTHhRj7YzlCewKAH+niUmIB+BnYx5HadTh4dmeIwu4PW/Ce5nddZsRE\n4Uht2RZAHBFdMfK5Xdozjx6yyv0pL/jqIYTwBPA7gLFElDeJxkkAVYkoCMCPALbYWj4NzxFRMIDO\nAN4RQrSzkxyFIjgosDuADQY+dpT2zAXxHNphXeCEEJMAZANYY2QXe98fC8Cmh2Bwbq9vbXx9cxmA\ngkf9Nm/PgvSQlPentZW/ZKkhrI0Qwhnc4GuIaFPez4noCREla55vB+AshPC1sZggonuax/sANoOn\ne/o4RHtq6AzgJBHF5f3AUdpTQ5zWNKZ5vG9gH7u3qxBiGIBuAAZplEA+TLg/rAoRxRGRiojUAJYY\nub7d2xIAhBBOAHoDWGdsH1u3pxE9ZJX709rKv1ikhtDY/X4GcIGIvjOyT0XNfhBCtAS33UPbSQkI\nIUoJIby0z8GLgHmT49m9PfUwOqpyhPbU4w8AYZrnYQC2GtjHlHvZagguqPQRgO5EZDCXsIn3h1XJ\ns77Uy8j17dqWerwA4CIR3TX0oa3bswA9ZJ370wYr2F3Aq9bXAEzSvDcawGjNcwFgnubzMwCaW1sm\nAzI+B55KRQGI1Gxd8sj5LoBz4FX0owDa2EHOmprrn9bI4pDtqZGjFFiZl9F7z+7tCe6MYgBkge2i\nIwD4ANgN4AqAfwF4a/atBGB7QfeyDWW8Crbpau/PhXllNHZ/2FjOVZr7LgqsfPzt2ZbG5NS8v1x7\nP+rta8/2NKaHrHJ/yhG+MjIyMk8h8oKvjIyMzFOIrPxlZGRknkJk5S8jIyPzFCIrfxkZGZmnEFn5\ny8jIyDyFyMpfRkZG5ilEVv4yMjIyTyGy8peRkZF5Cvk/8GKpOg1BOLgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x119730cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for i in stu_id:\n",
    "    s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==i)]\n",
    "    print(i, len(s2.correct))\n",
    "    print(s2.correct.values)\n",
    "    [prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "    plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "[0 0 1 1 1 0 1 0 1 1 0 0 1 0 1 1 1 1 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nKDHI/wMkuC1o74UNRwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1182c75c0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18226)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "20\n",
      "[1 1 1 1 1 1 1 0 1 1 0 0 1 0 1 1 1 0 1 0]\n",
      "stability stop at here 9 th problem\n",
      "similarity stop at here 10 th problem\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzs3Xd4FNX6wPHv2U1PSIDQAyF0aaGXoDQriooFREDUKwKi\n3HsF9QqiEEQUxV4QEb0gYoefhaui0hUIvfcmhJKEECA92ez7+2NIIwkJZCEJeT/PM092Z2fOnNmc\nfefMmTNnjIiglFKqfLGVdAaUUkpdeRr8lVKqHNLgr5RS5ZAGf6WUKoc0+CulVDmkwV8ppcohDf5K\nKVUOafBXSqlyqNDgb4z51BgTbYzZVsDnxhjzrjFmnzFmizGmreuzqZRSypXcirDMLOB94LMCPr8V\naHRu6gR8eO7vBVWpUkVCQkKKlEmllFKW9evXnxSRqsVNp9DgLyLLjTEhF1ikD/CZWONErDbGVDTG\n1BSR4xdKNyQkhHXr1l1UZpVSqrwzxvztinRc0eYfBBzJ8T7y3DyllFKl1BW94GuMGWaMWWeMWRcT\nE3MlN60ut1Wr4JVXrL9l3dW0L0oVoCht/oU5CtTJ8b72uXl5iMgMYAZA+/btdTjRq0FCAsyfD0OH\nQno6eHrC4sUQFlbSOStYcjIcOQKRkdbfnNPu3XDggLWcmxu89ho88ggEBJRsnpVyMVcE/x+BkcaY\nr7Au9J4prL1flSEiEBMD+/fnP0VF5V4+JQVuvx0GDoRbb4UePcDH58rld/ly+OknqFcP/P3zD/Cx\nsXnXq1oV6tSxDl7GWPvtcMDo0fDUUxAaCtdeC9ddZ0116uRNQ5VeK1ZYU8+epbticgWZwsbzN8Z8\nCfQAqgBRwATAHUBEphtjDFZvoF5AEvAPESn0Sm779u1FL/iWEiLw44/WVKWKNS9ngI+Pz17WGKhd\nGxo0yJ4yMuCll6xgabNBhw6wcaNVw/b0hO7drQPBrbdC48ZWGq6Qng7btsH69bBuHSxdatXcz1ep\nkhWs69Sx8p75Ouc8Ly9r2VWr4IYbIC0NPDzg1VchLg7+/NP6LCHBWi44OPtAcN110Ly5te+q5KWk\nwObN2eVi+XKrHAO4u8PPP8ONN5ZsHovBGLNeRNoXO52SepiLBv8SJAJ798KyZdYP47ffIDo6+3M3\nt9zBPedUr152oMxp1Sor+PboYdWsUlKsmtYvv1jTrl3WciEh2QeCnj3Bz69oeXY4YMcO68e8bp31\nw968GVJTrc8DAiAwEA4etPbPZoN//xtefLHo2yhoX3LmYcsW+Osv62CwYgUcP569/S5drANBxYpw\n8iTcdJPWMi+31FTrf5IZ6NevtyoEDof1eZUq1sF/3z6rXIB1UB82DEaMgGbNrlxeV62C//0Pevcu\nVrnQ4K+Kzum0AmdmsF++HE6csD6rVs2atm+3fhx2O0ycCOPGuTYPhw7Br79aB4JFiyAx0foRdu1q\nHQh69YIzZ6w8Xned9YPNGeg3bbIOKAAVKkC7dtC+ffbf+vUhIiJ3rX3RossbfEWs/frzz+xpx47s\nz93drTx07Xr58lAeZB6Mr73WOpDnLBdbt1pngACVK1tlIWe5qFMHVq/OLhdublalY/Fi632PHvDE\nE9Cnj/X/crX0dKtcfPwxfPWVVWa8vYtVNl0V/BGREpnatWsn6jJxOETWrxd56y2Ru+4SCQwUsYqd\nSFCQyMCBIh99JLJzp4jTKbJypYi3t4jdbv1dufLy5i8lRWTRIpGnnxZp3jw7b8Zkv86c/PxEunUT\nGTVKZO5ckd27RTIyCk575UqRl1++/PtQkOefF7HZsvNfrZrIkiUlk5eyzukUmTVLxM0tb7moWFHk\nxhtFxowR+fZbkYMHreULcn65iI4WmTJFJCTESq9mTZEJE0SOHi1+vqOirHz36yfi72+lb7dn591u\nt/JyiYB14oIYrMH/arB8uciIESKPPy5y223ZBQ5E6tcXefhhkf/+V2T//oJ/ICUZNA8fFrn77twH\ngX79RHbssA5kZUnOA6mHh0iNGtY+DRwocuzYlc3LihUikyeX3IHwUh04IPLSSyJNm+YO+MaI3H//\nhcvxxXI4RBYsELn1Vit9u12kb1+RxYuLvg2nU2TDBpEXXxTp1Cm7ElOzpsijj4r83/+J/PGHyypY\nGvyVyJEjIg8+mPsHUreuyLBhVi35yJGSzmHRXemzj8sp54E0KUlk/HgRT0+RChVE3nxTJC3t8m5/\nzx6R++7LLhNl4fuMjhZ5/32RsLDsfF93ncgzz4h4eV2ZcrFvn3U2Wrmytf2mTUXee0/k9Om8y8bH\nW0H90UdFatXKPjh16mQdBNavz3vwcFEFS4N/eeV0iixdKnLvvdYPImdTSTFPJ0tcSTfZXE5791q1\nSxBp0UJk2TLXpp+RIfLzz9nbyNn0BFbzSGkTHy/y+edWnjObRVq0EHnlFasZJ9OVLhdJSVazTceO\nVp58fUWGDxf54AOR22+35nt4WJ/5+1tnqbNmWc09V4AG//ImMVFkxgyRli2tf1vlyiL/+Y/IvHlX\nT435aud0inz/vXV2BiIPPCBy/Hjx0oyLs67tNGxopVmjhkh4uMiPP1rlIfMgUKWKyMaNLtmNYklL\ns5pZBgwQ8fGx8lanjsizz4ps2VLSuctr7VqRf/wjO9hn1vAHDLCahi73WVw+NPiXF/v3izz1lHWB\nC0RatRKZOdM6GGS6mmvMV6PERJFx46yA4u8v8vbbIunpF5fGtm0ijz1m1UpBpEsXkS+/FElNzV4m\ns1zMni1Su7a17IIFrt2XwqxcaV13mD7dui6V2fmgcmWrNr18+YUv4JcWOS/kl/AZtgb/q5nTKbJw\nocgdd2RfhLrvPusCnqsudKmSt3u3yM03Wz/Dli2t/++FpKeLzJ8v0rOntY6np1UrXb++8G0dPSrS\ntq0VwN591zX5L8yKFSLu7tk1Zg8Pkf79rbOSnAepsqAUXZPS4H81OnvWusDUpIlkdRN8/nmRyMiS\nzpm6XJxOq+muTh3rf/7ggyInTuReJibGagcPDraWCQ62uinGxFzcthISRO6800rjn/+8vD2pVq/O\nvhCaeQ1iwoTLt70roZScYWvwv1qsXGn1Ye/b1+oNAtYFpTlzrP7wqnxISBAZO9aqKfv7W2Uis+uu\np6dVLq6/3qr5X2wTUU4Oh5U2WBcv4+Ndtw8i1gHp0Uet9AMDrdp+KagtX000+F8Nfvst980fvXqJ\nRESUdK5USdq1S6R9++wyAdY9ENu2uXY706ZZtfHWrV3TJdjhEPnwQ5FKlawy/dRTImfOlJra8tXE\nVcFfR6IqKStWwH33WYOigTWsQrdu0LFjyeZLlawmTeDuu7MHibPbrYHymjd37XZGjIAFC6wxbzp1\nsgbiu1QREVYaI0ZAq1bWmEuvv26NqhoWBmPH6hhHpZAG/ystPR2ef94aU8TX1xr10m63xqLp0aOk\nc6dKg549r0y5uPVWa5A6m80af2jBgotb/+RJ6zkOnTvDsWPwxRfWmDmuPlCpy0KD/5W0d681ONXk\nyfDww7BzJyxZApMmXf5ByFTZERZmlYcrUS5CQ62ae5Mm1uBm775b+DoZGTB9ujU893//az3vYNcu\nGDDAdcN1q8tOR/W8EkTgk0+sIYY9Pa0R/u69t6RzpVS2xEQYNAh++AH++U946y3rzON8a9bA449b\nI2r26AHvv681/SvMVaN6as3/cjt5Eu65xzo9Dguzxh7XwK9KG19fmDfPenLZe+9ZZwE5H+KjTTxX\nHVc8xlEV5LffrOad2FjrAtioUfq0J1V62e3wxhvQsKFV++/aFSZMgNmzrUCflGQdHMaPty7mqjJN\ng//lkJJi9XB4+23rSUG//GL1glCqLBgxwnpi2733WmetYFVaZs+GBx4o2bwpl9FqqKtt3Wp113z7\nbRg50nrikAZ+Vdb06gWPPpr93hg4cqTk8qNcToO/qzid8M47Vp/s6GjrIdHvvWc9sk2psuj++63y\nq12Rr0ra7OMKx4/DP/4BCxfCHXfAzJnWc3GVKssyu5zm9zB7VeZp8C+OVatg2jT48Ufr5q0PP4Th\nw7Wvs7p6hIVp0L9KafC/VKtWQffuVtA3BubOtW5yUUqpMkDb/C/V229bgR+snhCHDpVodpRS6mJo\nzf9SLFxo3RBjs1m1fr0YppQqYzT4X6w1a6z+zy1awNSpVldOvRimlCpjNPhfjN27oXdvqyfPL79A\nzZpw000lnSullLpo2uZfVMeOwS23WM08CxdagV8ppcoorfkXxenT1h2PsbFWn+dGjUo6R0opVSwa\n/AuTnAx33mmNV/6//0G7diWdI6WUKjYN/heSkQEDB1qPXPzyS23fV0pdNbTNvyAi1kMrvv/eGrPn\n/vtLOkdKKeUyGvwLEh4OM2ZYQzP/618lnRullHIpDf75mTYNXnwRHnnEet6uUkpdZYoU/I0xvYwx\nu40x+4wxY/L5PMAY85MxZrMxZrsx5h+uz+oV8t131jj8d9wBH32kg7Qppa5KhQZ/Y4wd+AC4FWgG\nDDDGNDtvsSeAHSLSCugBvGGM8XBxXi+/JUush1h36QJffQVuej1cKXV1Kkp06wjsE5EDAMaYr4A+\nwI4cywhQwRhjAD/gFOBwcV4vr40brYdWN2pkDdHs41PSOSoTXnsNGpw5Ra25u0k9nIpnsCfHBjVh\nf0Bl/vOfoqcTNTeKA+MOZKVRf3J9qg+qfkXz8dprUOfkCSp+tBHPs56k+qdyZnhbDlepXuQ0irsf\nV1MapSEPpaV8uuK7cLWiBP8gIOfz2yKBTuct8z7wI3AMqAD0FxGnS3J4JRw4ALfeChUrwq+/QuXK\nJZ2jIikNAa/BmVMMedmPCXjThlRW/+3NxJf9+OS5U0DRvscXBsQT+H8naJ2aCkDq36l8OeQEsQt8\nmPRlhSKlUdx8iAjuWzbz2NxmhFOTNpxm19maTJxaiQkDN+FMDUVEzi18bjr3OnN++MNJVPnpBK3T\ncuzHIyeIme9F+Cc+2etAga+jv41m/+j9OJOdWWnsHrobR7yDqvdWzb0e+acx8bFkqv6cTz6+82T8\nh0V7slzM/BgOPH0gTz7ST6dT9Z6qRVv/mXzWjyva+gAvPp5M1V/y2Y9vPRk/rWj7ERJ5liHvBeQp\nFzP+GUPqsaI9hD5mfgwH/nPp+5Lvflxk+b4cTFaBLmgBY/oCvUTk0XPvBwOdRGTkectcC4wGGgC/\nA61E5Ox5aQ0DhgEEBwe3+/vvv124K5coKgquvRbi4uDPP6Fp05LOUZHNG5cZ8HbQhtNspCITacYn\nzyVw7+SiBd43B21i4hfNeIo9NCGezVTkPRoytMsBej0YREqikJIkpCZBSrKQkiykJmNNqcLxX+M4\nnObDUj8Hbn374/juG25LEJq4xxPUtQJ+bhn42jLws2XgYxz4mQw8xIk4JGtattJGeHrTPPsRbttJ\nl8apiFPASa6/jgxDcoYhOcNGSoaNhFNOthDAJ9SnE7GsJpB7OUIdUshwt+EQG+lAutOGA0O6GBxi\nvXZgSMd6HYUXm/yScevbn4zvvqZ7gp16JOFNRqHTDiowiWZ59iPz/ZVy/nZLIh9fUodriM+1vY1U\nZBcVGEDRngV8KfuRjiEVO8nYSMVOCja2EMCn1KcbMfxJFcayk86coqhX84q7LwXtx0vV9zLyRPMi\n5iKbMWa9iLS/6BXPT6cIwT8MCBeRW869HwsgIq/kWOZ/wBQRWXHu/WJgjIisKSjd9u3by7p164qb\n/+L54w946CE4eRKWLYPOna/Ypotba3c6hcXBa1lytALv0JCOnCKCQHpzjOreDtw7VyI+3pCQhDWl\n2EhMMSSm20hKt5HosJHstJOEHWeRfwYX0HsEtJsB64fD/6ZdcFF348TXnoGfWwZ+bk68ElJJx8Ye\nKhBMIofxpTHxeOIkI9DTCvCOc4HeYSM5w+BwFr+jmsGJDUfW5IcnbgixvZ/E0e5jzPphmP9Nu6jv\nx50MHNiogIMk7LTjFC04S+gDAQRVchAc6KSafwZ2G+RK9tzrSf9KLTDQjH/fK9ey+ewQIrDpiYP8\nRSDv0ZAbiWIp1bICT6MPizY0yd4Rewv8rPH0xoWu//Vj0QUG7v4f5X7EqQgkphriEmycTrIRl2Aj\nLtGwe+ZJdlOBxVSjDkkcxpdG58qFqedLcpohOc2QlGqyXjucRftfuZNB1UpClQpOAs9N2a8zsl5X\nqeBkyXMneIXsyskGKjKR5jzJbnpOqMaZZEN8so34ZMOZJBvxyTbOJhvOnpt3bE0yUXhyGF+8ycAN\np5WWOU0PZ48i5TenKxn83YA9wA3AUWAtMFBEtudY5kMgSkTCjTHVgQ1YNf+TBaVb4sF/xQprKGan\n0xqPf+nSKzosc0G19teHxdPyZm+OH8jg+CEnJ445iT4B0bGGmDM2YhPtxKa4EZfhRhr2C27Dkwy8\nTQZexoGXScfTpOFuUrCTjHEmIhlnaEEIPjjZTABrCORaYriRaNzIYA4zsbs78PASPHwET1/w8jV4\nV7DhE2DHN8CNOQ3eJ909Pc+23dLdqfPF7URFpZCUZAcCzk3+QABuboF4e1fH3b0q1U81JBVvovAk\nEXcqkE51UrCTSmyVIzid8aSnnyU9PY7U1DhEEoCkc1MikMQohpNITeb4uePVtz+p333DEwkJ1DaH\nmNf1S3x93fH19aBCBU+8/ewYv3QyfNNJ9UglyT2JBFsCv8T+gtOWt7XS3eHOnTsfISoqkZiYZGJj\nUzl1Kg2n0xvrEpc13cvD2AlgIxXZSwWqkEoGhjhy931wd4fgYKhbF0JCsqe6dWHpvbt582S9POXi\nP1UO0vuPJkRHWyerUVFkvc75Nzoa0tJy59+Gk0YkUN8rkV6v1iQ0FEJDL9y6uSpkFal/p+YtU3U9\nCTtU+O9kad3VLDwcwHs0oiWn2UQlOnMSP1+D/cZqnDplDZUVGwunTmU/Fyk/bjhxYMP/XLmwkUps\nlcNkZMTjcJwhLS2O1NTTQN5yMZrhJFCTOX4eePTtT+p333JjggHOssh7C97edbHba5CeXpmkJF/S\n0vL/TWUe1N1xksb5R+687HYnPj5puLsnU/GUVfJP4sERfBnE3zzKQeJ947kj4Y5Cv8vzXbHgf25j\ntwFvA3bgUxGZbIx5DEBEphtjagGzgJpY38oUEfn8QmmWePC/7TZrWGYAux0mTbJu6LqMnE7h2M50\ntixJZdmzJ1iT5M+fVMUPB2dwB0DyKVR2nFSypRPo6SDQJ4OKvmn4eiZQYV8yyeLDjwTRjWj+pCpP\nsod67GEI/RGyf1He3t5Ur16datWqUb169azX7V9vz6G0ekzwq4R33/tI/u5bJiacoqHPEXrH90YQ\nIs9Gcuj0IQ6ePsjBuIMcPH0w633kmci8v4MUP+qfaMmrz4ymR0gPvJxeHD9+nGPHjnH06FGOHTuW\nNR09epSAtQF0TR3PK7TmTo7xI7UYyyaWuYdzvPlxAgMDqVy5cq6/579+8a4VLNj1IE1vv5817b6n\n6e6uHNj5INc0/Jku/6jB0fijHI0/SuTZSKISopDzGs897B74pFTmdKoD43MKyXkQEENojZZ0DupM\np9qd6Fy7M00CmxB/Np7o6GhiYmKIjo7m16d/pf6BIXn241cmssrzKA0b3kitWmH4+TXH6QwmKsqH\nQ4fgxIncX58NJ2CoQDpncT+X07zlwsMDqle3RhivVk3w8YnHmBiOLFpHSFw7FhBCK+JYR2WCSeCY\nu52kdL+s9YOCoFUrsg4GrVpB48ZWJzfrOswBWqfGZS2/ybMSsXfXZ9KXFUhPhyNH4OBB6yF2Bw/m\nfn38eN7y74GTypWEqrXtBAaCv78DD494IJb09CiSkyM5e/YQsbF7OHFiO63i69CL/zCVVnnKRVTL\nqCKVi/A7l2WXi7bfE7a+L9v+9znXVnub6rftZM+ePezevZvY2NhzufTFbq9FrVqtqVGjBZUqXYPb\n3zXx3F2HzVThAH604DQdiSK6wR7ia0dy9uwR4uIOEhOzj8TESOAMkJy13zfbbuZ656Q85SLp2v8x\n8c+Jeb+oQlzR4H85lGjw378fmjUDhyP7SVyLFhW55l9Yk03yWSfbl6aw7a90dmxysne/YX+UG4cS\nPYkX96x03HHii4PTeNCQeMKIpRLptHiyGpVrpuNwP8Zp526OnNrKnr272L17N3v27CElJQWANoxm\nP68QnqOWGE4zutV8jyHTGuQK9r6+vph87ll4a/AmJnxVn5B77mZb0yXUPtqcE9EdCLlmOxlBJzl8\n5jAOZ3bHLYMhyD+IehXrUa9SPbb/WZG9p74nPvgI9gw7DrsDj7P+SIUM0m2JALSu0ZrrQ67nhvo3\n0DW4KxU8c1/kuumml1n2x+M8ywZ6AkuAV2lL9xun8fvvz+X7PxARjpw9wuYTm9kctZnxiycgJv8+\nBpW8KhHkH0RQBWuq7V87+72/9T7QO5CpUw2/n32IRfY5uGe4k+6WTquM6wn0uha3uhFEHI3gdIrV\nHOPv6U/HoI5ZB4ROQZ0YeNfHLPvjcR73+50lfV/i+u9e4IOEG2nSYjw33mhn9erVbNiwgbRzVfOg\noCA6d+5M27ZdCAnphp9fC06c8GLLj2f46Wcbh6UCDW1n6H07NL4lgOrVwds7noSE/Zw8uYMjR7ax\nZ89udu3axb59+7LShR648395vs+AKsMYN+4eatXqxeHDFdm8GbZsgZ07s2venp7WT6NqVfhzmZMH\nvY9Q8XQSm30DWZxalSbNDKdPQ2SkddKcyW6HOnWgXj3rLMaYQ6yYs5aj6X24hSj+oBpN7RPw7baG\npKQkDh48SHR0dK7/k5eXF/Xq1SMkJIR69erx++8ODu199aLKhVOcHIw7yOaozWw+sZlJy17Kt1zY\nxRNHeErW+9jYWPbu3Zv1G8s8KOzdu5eUlBTaMJp9TKYPx/mBmjRkHBt5k6CgIIKCgqhdu3bW6/On\nu+56J99ycaH9uBAN/pdKBG680XoC15w5sH37RT+JK7PJZiw78cbJCgL5iSAa+iRzOs2N4w7PXO3E\ngbY06vun0qBWBo0bC83a2Ml4ewfH47yYRLOs2sAEdlDX/QAjq4zkeI6qk81mo379+jRp0iTXNHLk\nYdy3beI/dKAa1YgmmtdYC206sGFD/mMRiQj74/YTERnB6sjVvL/2A/LvQmK4v0V/K8hXrEdIxRDq\nVapHcEAwHvbczRh9vuhD5K5I9n65l8YDGhN0TRDf9f+OdcfWsejgIhYfXMxfR/4iLSMNN5sbHYM6\nckO9G7i+3vWE1Q7j3yOP8uWXo0nzWEjK7Sl4LfDCI+0WBgx4k+nT65Ocnsz2mO1ZgX5z1Ga2RG3J\nCsQAwQHBZGRkcDz+OE7jxC52bml4C+/1fo/6leoX+X97z9f3UNOvJsPaDWPG+hkcTzjO/P7zASuw\n7Indk/XdRRyNYEvUFjIkAwB/RzBJe51k+B1Fagv2jXZ8l96etR8AqampbN68mdWrVxMREcHq1as5\ncOAAAHa7nfr163PoUD3S0z8HPgRG4O7+AE2bnuDEiRO5AqabmxsNGjTgmmuuyVUuRow4wNatnwJL\nc+xZD9zdryU9fTI2m40uXbrQp08f+vTpQ926jdi1yzoQZE6bN+c9IwkMhGuuyQ7w9eplT7VrQ0ZG\nKhs3biQiIoJXXllNVNS7wH3n8tED+AYvr4e47rr0rACfM9hXr149VwXlsccOXLBcxKfGsy16W1ag\n3xy1ma3RW0lISzhXgg31K9UnzZHG0bNHcRqnVdQNtKvZjiFthtC/RX8qexfc/uV0OunePZw///xn\nnn254YYZ/PHHuELLVOZ+JN6wgIzQjHzLxcXQ4H+pPvkEHn0Upk+H4cMvatXks04Wz07ku2dOsTw1\nkAP4knk67kYGdUmmYe0MGoU4adrSRrPOdkKv96Jy7ewetXFxcSxbtowPHtnBmrin89TaW3pNotH9\nkbl+zA0aNMDT0zNPfpYvX84999xDoknM+nH4ii/z58+nW7duAJxOOc2ao2usgHV0NRGREcQmW6e4\nvu6+hFYP5XTKafbH7SctIw1vN2/uaXoPr9/8OjX8alzil5zPd5eezMojK7MOBmuPrcUpTrzcvLgu\n+Dq61u7KF0u+YLdtNw2kAYO7D2bnqZ1sjtrMntg9OM/1HPZ196Vl9Za0qt6KVtVbEVo9lJbVW+Lv\n6c+IBSOYsWEGHnYP0jLSGN5uONN6X/jic3ElpiWy/vh6IiIjGLNoTFY+c/Jy8yJ5XHI+a1tiYmKy\nDgTTpu0kLm465weagIBh9O0bSJMmTbKCfb169XB3d8+TXkHlYt68eVSoUIEffviBH374gc2bNwPQ\nrFmzrANBhw4dsNmsi+kHDybSp89Otm5tT1jYUv74oyM+5+5/EREOHDiQle+IiAg2btxI+rlTCHf3\ncaSn/8X5B6A2bYYXWDHJT1JSEtdNuY6Nto3Ul/oM7DqQHad2sPnEZvbH7c9aLsAzgFY1WhFaLZRW\nNayy0bxac3zcfXKVi1RHKp2COhGfFs/2mO142D24vfHtPBj6ILc2ujVPxQagbduv2Ljxo4velzMp\nZ1hxeAV3fXVXVgUhp8LKRUE0+F+KY8esc9rWrWHxYusB7BeQmuRk+edJ/P5dOsvXu7HplA+p2LEh\nNCIeO8IOAriHSJ5gHzZDnqv38fHxrFixgiVLlrB48WI2btyIiGDMs7SW9IuqtecnKSmJrlO6ssG2\ngTbONnzwjw/YHLuZiKMRRERGsPPkTsCqBTWt2jRXu3Wzqs1ws7mVSNA8k3KG5X8vZ9HBRbwb8W6e\nNvjMPN/Z5E5Cq4dawb5GK+pXqo/N5P9/u1Ct/Uo4Hn+cp397mvm75pPiyG5S6BbcjVdufIUudboU\nmsalBprz5SwXbZ1tWTFmRVbgznTo0KGsA8Hy5cvJyMigZs2a3HHHHTRo0ICXXvqL+PiZZJ6B+Pj8\ngwEDanDixAkiIiI4edLqz+Hj40P79u3p3LkznTp1olOnTowZM4bPP8972e+BBx5gzpw5heY/MS2R\nSq9WIt2Z90qwwXBvs3tzVQCCA4LzbdaE/MvFvPvmsenEJj7b/BlfbPuC6MRoAr0Dub/F/TzY6kE6\n1OqQlV4uVYceAAAgAElEQVTmwTQpKYnk5GS8vb3x8fHJVcnKzPOfh/9kyaElLD64mPXH1+MUJx42\nDyp6VSQuJY50Z3qxK1ga/C+WCNxzD/z6K6/98wgN3G152uz3+laiS3ASC79KY/kaO+tjfEg+dx9c\nQ68krr0mlRtvs1Nt1i62H/Ng4nlNNp3rJtNqRytWrlyZFezXrl1LRkYGHh4ehIWF0bNnT66//no+\n/PBDvvzyyzzZLOqPA8B7sneuIJNTFZ8qdK7dmU5BVqDvUKsDAV4B+S5bGoLmyJ9HsmDvAtIy0vC0\ne3Jnkzt599Z3XXr2cSWcfyDtFNSJfaf2EZMUw80Nbia8ezhhdQpuYhw8eHCxgiYUXC4uVNM8deoU\nP//8Mz/88AO//vorCQntgW84/wwE7qNp06isQN+5c2eaN2+O23lDoRQ1YOZ0+MxhFuxZwII9C1h8\ncDGpGam42dwQETIkA0+7J3ddcxdv93rbpeXC4XTw2/7f+GzzZ/yw+wdSHCk0CWzC4NDBPBD6AHUr\n1iUpKYmxk8fyQfQHjKw+kpefexnjblgVuYolB5ew5NASIo5G4HA6cLe506l2J3qG9KRnSE/C6oQx\n6tdRLqtguSr4IyIlMrVr106uqG+/FQGRV1+V756LlQBS5XU2yieskbs4Im5kiBfpYh0lREI8kmRA\n8ziZOfq0RO5IzZXUmw9sFH9S5U02yhKWyJtY73tVfVU8PDwEELvdLmFhYfLcc8/JH3/8IUlJSbnS\nWLZsmQQGBoq3t7cA4u3tLYGBgbJs2bJCd+XAqQPy8vKX5Zr3rhHCyZrcXnSTLp90kdVHVovT6XTp\n13e5PfbTY2KbaBOvl7zENtEmIxaMKOksXZK7v7pbHl/wuGw6vkkeX/C43P3V3ZKQmiBT/5oqVV+r\nKoQjt8y5RVYfWZ3v+sUpF9ujt8sLi1+QkLdDcpUL+0S79P26rxyPP16kfUhOTpb69acL9Mi8l/nc\n1ENCQz8v8neRmJgozz33nFSsWFHGjRsniYmJuT7PcGbIqiOrZNyicRL6YWhWfhu800Ce/OVJWXRg\nkQz9cegVLRenk0/LzPUzpdt/u2Xlp8esHvLJhk/k4e8fFhNupP1H7aXHrB7iOckz6/vt9HEnGfP7\nGFm4b6EkpCbkSTe/cnGpgHXighhcPmr+p05ZzT1BQRARwTdBW/goOoglVMvqWhlIKp3dTtF7mA+9\nhnhRr23eNvZMbdp8idm0Lk+TzS7vCjzxRCLXX3891113HRUqXPjW7aSkJCZPnsy0adN44okneO65\n5/Kcmmc6Hn+cb7Z/w5fbviTiaAQAXep0wWBYeWQlnm6eV6zJ5nIo6bOPKyExLZEP1n7A1JVTOZl0\nklsb3kp4j3A6BnXMtdzFlIuDcQf5evvXfLntS7ZEbcFmbPQM6YnD6WDF3yuw2Ww4nA683Lz44p4v\nuLvp3UXKqyvOQMAqt/fPu5+v+35NDb8axKfG8/uB3/lpz0/8vPdnohOjsRs71wVfx+2Nb+eOxnfQ\nOLBxVpNLSZaLQ6cP8fmWzxm/ZHy+zZJ2Y+eH+3+ga92u+HsWbagIV9Ca/8V4+GFJNn4yY+Be6Vjp\nrICIDafUJlFA5B6OyBKWyBKz5ILJnDx5Ut566y3x9/c/r0ZkTQ888IBLsx2bFCsz1s2QnrN6igk3\nQjjSenprmbJiihyMOygirq1RqCsjPjVepqyYIoGvBgrhyG1zb5M1kWuKvP6xs8fkndXvSOeZnbNq\np2Ezw+Td1e9m1e5zlov7vr1PAl4JEMKR+7+7X2ISYwrdRnHOQHIasWCEmHAj135yrdw852bxmOQh\nhCMVp1SUAd8NkC+2fCGxSbEXleaVdvTMUbn5s5vF/UV3IRzxfslbBs0bVOQzKVfDRTX/qz74b37l\nTxnKQqlIqoBIdXuKDPE8KOPZKgGkymAOSsC5JpyVdVfmWd/pdMrSpUtl4MCB4unpKYAEBga6LPgf\nO3tMuv23W1ZBik+Nl883fy695/YWtxfdhHCk0buNZPzi8bIjekexvw9VesSnxssrK16Ryq9WFsKR\n3nN7y9qja0Ukb7mITYqVj9d/LNfPvl5sE21CONLqw1a5KgIXkuZIk0nLJon7i+5SbWo1+W77d4Wu\nU1izzYV4veSVq+kpZxPUskPLJD0jvchplQalqVnSVcH/qmz2ST7r5LPnz/LJZzbWnvHHhpOeNc4y\n7Akb9/ynAj9MjCt0QLSTJ08ye/ZsPv74Y3bv3o2/vz+DBw9m6NChnDlz5qIvZhXk8f89zkfrP+Km\n+jcR4BXAT7t/ItmRTB3/OvRv3p8BLQfQpkabAnsyqLIvPjWe99e8z+urXudU8inuaHwHnnZP5u+a\nT8+Qnni7e7Nw30LSnek0qtyIAS0GcH+L+2la9eIHIdwatZWHf3iYDcc3cF/z+3j/1vep6lu0UTaL\nIsWRwpzNc3ht5WvsO7UPg0EQvNy8uLfpvS7vQnyllKZmyXLb2+dCd9fe3DKZ919I4bsNfpwRd2qa\nJB6QBTw+M5iQIZ1zpZHfMMZ/B1ajY8elzJgxg/nz55OWlkaXLl0YNmwY/fr1y9XuejHtsvkpqEeG\n3dhZ+vBSutTpUmCXRnV1Opt6lsDXAnPdUZ3JbuysGbrGJRWB9Ix0pq6cSvjScCp6VWRa72n0bda3\nWGnGJMYwbe00Plj7ATFJMbSt2ZZKXpVYcmjJFe1CXB6U2zb/zJ46mT1tXmWT+JAuDT0SBETsZMhN\nQaflu8e2iQO7yOOP50kjsz3Tx8cnqz3Tx8dHateuLYBUrFhR/vWvf8nWrVsvKY+FORF/IqsXQ+bp\nsOckTxnw3YASa0dUpcOxs8ek3zf9stqXPSd5ysB5Ay9LudgatVXaz2gvhCP9vuknUQlRF53Grphd\nMvyn4VnNPL3n9pYlB5eI0+nU61GXCS5q9ilzzymsNXc3E/BmAs2pTRK78EcwJKWlM6ZXHI9P9aVO\nY29o2x9q14RXXsmTxscff5xjICdITrb6Pvv6+vLZZ5/Rt29fvL2L9rCIixGdGM1rf73GtLXTSMtI\no2HlhuyN3ZvVU6eiV8UyeUqsXKdmhZoEegeSIRl4uXmRlpFGgGfAZSkXLaq1YNWQVUz9ayrhy8JZ\ncmgJH9z2Af2a9bvg2YWIsOLwCt5Y9QY/7v4RT7snD7Z6kFGdR+VqisrZLPJB7w9cnn9VPGWuXSHq\nbyfLqEo8buwkgGCSeJ1NzCGCV36pRJ0WHlbA377dGsLBP28XrLi4uHxShltuuYXBgwe7PPCfTDrJ\ns78/S7136vHW6rfo26wvO5/YSfOqzRnRfgSrh6zmsXaPcSLhROGJqateVGIUj7V77IqUCzebG2O7\njmXDsA3Uq1iP/t/1p9+3/YhKiAKsrprdZ3XnRMIJHE4HX2/7mo4zO9J9Vnf+OvwX47uN5+8n/2bG\nHTMu6RqEKkGuOH24lOlim30S4hwyptcp8SVdDE7xwCF9OZy3p862bSLu7iIDB+ZJ49ixYzJkyJB8\ne+pwGbpqnkw8KWP/GCu+k33FhBsZNG+Q7IrZ5dJtKOUq6RnpMmXFFPGY5CGBrwbKl1u/tHq5hNuk\ny8wuUvetulm9zz5c+6EkphW9949yHcpLbx+nU5jx77NM/NCTExletPBJ4O8kTyayPW9PnRcDrEcy\n7t8PO3ZY49ICiYmJvPHGG7z22mukpaVx1113sWjRIpKTk4vdWyc/p5JP8eaqN3k34l0S0hLo36I/\n47uN15qRKhN2xOygxbQW+d7Y5G5zJ+X5FO2MUIJcdcG3VP8HF36YQKh/EiPeD8Dfw8n3r8czeIIf\n/30uns51k8FA57rJfPJcAvsDKsN770FEBLzzDlStitPpZNasWTRu3JgJEybQq1cvduzYwTfffMOR\nI0cYNWoUFStWZPTo0Rw+fPiiA3/OU2KwRtCcsGQC9d6px+QVk+nVsBdbR2zly3u/1MCvyoxmVZvx\n95N/07p666x5nnZPBrUcxOFRhzXwXy1ccfpwKdOFmn22LUqWm4JOC4hUsaXIW0PiJD21kLFqDhwQ\n8fERue02EadTFi1aJK1btxZAOnbsKCtWrLjw+pdgxIIRYptok0e+f0TCl4Rn3UV579f3ypYTW1y+\nPaWupNJ0Y5PKxtXY2yf6YDpj+iYyZ4M/7rgzqmcc4V/541+t4oVXFIFhw8BmY9fTT/PMnXeyYMEC\n6tatyxdffEH//v2zxih3hfP76H+66VMAbMbGpuGbaFWjlcu2pVRJybzwnPPGJnX1KBVt/ikJTl4Z\nfIa3fvAjQdy4p/EZXv/am5DWBQ+ulsusWcT84x+Ed+vGR3/9ha+vL8899xz//ve/8fLycnnej8cf\n54H5D7Dk0BIEwW7s3NzgZj7t86l21VRKXVauavMvsZp//Pp4Vgav4qtajfhsbQBnnJXoXDmetz6y\n07nvhWv6iYmJTJ48mQ8//JBhgwbh98knvG63k/jXXwwfPpzw8HCqVnXdLes5xSXH8fzi51l8aDFg\nPfjb4XQQUjFEA79SqswosZp/XdNM3FjDAfyobkvhnQlp9Hu+AjbbhW9dz3xIRHJyMklJSRisfpph\nrVvzyRdf0LTp5buwOm/HPEb+MpKYxBgaVGpAj5AePN7h8RIf60MpVX6U+Zr/YXww+NKXw/wr6Chd\nxxftAern352beehq0KLFZQv8x+OPM/KXkczfOZ82Ndrw88CfaVOzTdbneveiUqqsKdELvv05wnAO\nkBFZtOWdTie7du26vJnKQUT4dOOnPP3706Q4UphywxSe6vIUbrZSdZ1cKaUuWolFscqk8Qs16Mgp\nOgcX/gT7Q4cO8cgjj3Clnv61/9R+hi0YxuKDi+letzsf3/ExjQIbXZFtK6XU5VZid2tUIZUJ7GAi\nzTg2qEmBy4kIM2fOpGXLlqxdu5ZnnnmGQC8vMkff8QYCfXwYOnSoS/LlcDp4Y+UbtPywJeuOreOj\n2z9i8UOLNfArpa4qJXqrXq67c/Nx7NgxevfuzdChQ+nQoQNbt27ltdde43DbtowCKgKj3dw4vGCB\nS4Zl2BK1hbBPwnj696e5qcFN7Hh8B8PaDdM7GpVSV51S0c//fCLCF198wciRI0lNTeXVV1/liSee\nsG7UWrUKunSB4cOhbl3o0QPCinax+HyZD5f+7K7PmLlhJlP+mkJl78q8d+t7hQ5rq5RSJaHM9/Yp\nSHR0NCNGjGD+/PmEhYUxe/ZsGjU61+QiAmPGQPXq8MYb4OtbrG1NWj6JFX+voPX01pxOPc2DrR7k\nzZvfJNAn0AV7opRSpVepCv7z58/nscce48yZM7z66qs89dRT2O327AV+/RWWL4cPPihW4D9/eIbT\nqacB+Gb7N8y+a/Ylp6uUUmVFqWjMjouL44EHHuDee++lTp06bNiwgf/85z+5A7/TCWPHQv368Oij\nxdrevPvm4efhl/Xe282bQS0HcfDfB4uVrlJKlRUlVvPftGkT48aNo127dowcOZKYmBgmTpzI2LFj\ncXd3z7vC11/D5s0wdy54eFzSNh1OBy+veJkXl72Il5sXBoOnmyepGan4e/rr8AxKqXKjxC74GmPE\nbreTkZFBSEgI8+bNo23btvkvnJYGTZtChQqwYQNcwgide2P3Mvj/BhNxNIJBLQdxJvUMwf7BuUYs\n1OEZlFKl3VVxwTcjIwOAsLCwggM/wMyZcOAA/PzzRQd+EeHjDR8zauEoPO2efHXvV/Rv0T/XMjo8\ng1KqvCkVF3xzte2fLzERXnwRunWDXr0uKt2ohCiG/jSUn/b8xI31b2RWn1kE+QcVM7dKKVX2Faka\nbYzpZYzZbYzZZ4wZU8AyPYwxm4wx240xy1yWw3fegagomDIFLqLf/U+7f6Llhy35bf9vvH3L2yx8\nYKEGfqWUOqfQmr8xxg58ANwERAJrjTE/isiOHMtUBKYBvUTksDGmWlE2nvng9AKHZoiNhVdfhT59\ninwjV0JaAqMXjubjDR/TukZrlty9hObVmhdpXaWUKi+KUvPvCOwTkQMikgZ8BfQ5b5mBwHwROQwg\nItGFJWq32wt/cPqUKRAfD5MnFyGbsDpyNW0+asPMDTN59tpnWT1ktQZ+pZTKR1GCfxBwJMf7yHPz\ncmoMVDLGLDXGrDfGPFhYoq1bt+all17Cx8cn/wUiI+G99+DBB6H5hQN4ekY645eM59pPryU9I52l\nDy9lyo1T8HQr4mMglVKqnHHVTV5uQDugN3AL8IIxpvH5Cxljhhlj1hlj1sXExFw4xYkTreEcJk7M\n9+Pj8cfpPqs7fx3+i2s/vZZJyyfxQOgDbH5sM93qFn+QN6WUupoVpbfPUaBOjve1z83LKRKIFZFE\nINEYsxxoBezJuZCIzABmgDWwW4Fb3LULPv0U/vUva/C2fLy47EVW/L2C7rO6E+AVwLf9vqVvs75F\n2B2llFJFCf5rgUbGmHpYQf9+rDb+nH4A3jfGuAEeQCfgrUvO1fPPg48PPPdcno/OH5cnQzI4lXyK\nwf83WIO/UkoVUaHNPiLiAEYCC4GdwDcist0Y85gx5rFzy+wEfgW2AGuAmSKy7ZJytHYtzJsHTz8N\nVavm+sjhdDDm2jHYTfZ9AT5uPjouj1JKXaQi3eQlIj8DP583b/p576cCU4udo7FjraA/enSu2RuP\nb2ToT0NZf3w9wf7BHDl7BE83T1IyUnRcHqWUukilYlTPLH/8AYsWWc0+FSoAkJiWyDO/PUOHjzsQ\neTaSb/p+Q7ta7RjRfgSrh6zmsXaPcSLhRAlnXCmlypbS8yQvpxM6doSTJ2H3bvD0ZOG+hYz43wgO\nnj7I0LZDefXGV6nkXalE8quUUqXBVTGwWy7z5sH69TB7NtGOM4z+32jmbp1Lk8AmLHt4mXbfVEop\nFyodwT89HcaNQ1o0Z3YLB0990JT41HgmdJ/A2OvG6s1aSinlYqUj+M+axb7YvQwfF8rin4ZwbZ1r\nmXHHDJpVbVbSOVNKqatSiQf/9PgzvP79U7z4hA2PtENM7z2doe2GYjOl61q0UkpdTUos+O+O3c3P\ne35mzFdD2Noxnr5Ve/DO4LnUqlCrpLKklFLlRsk9xrGWEYZDULxh2vG23Dl3XeErKaVUOXfV9PY5\nWkHoH7CV5JLOiFJKlSMl2rDukw6DzoZwcNTfJZkNpZQqd0os+BsgxQ7+7a/VoRmUUuoKK7Fmn6Yx\n0GOD4XiNyJLKglJKlVslFvy9HfDBLzbocktJZUEppcqtku1M7+EBPXqUaBaUUqo8KrngHxRkjeAZ\nFlZiWVBKqfKq5IJ/jRoa+JVSqoToGApKKVUOafBXSqlySIO/UkqVQxr8lVKqHNLgr5RS5ZAGf6WU\nKoc0+CulVDmkwV8ppcohDf5KKVUOafBXSqlySIO/UkqVQxr8lVKqHNLgr5RS5ZAGf6WUKoc0+Cul\nVDmkwV8ppcohDf5KKVUOafBXSqlyqEjB3xjTyxiz2xizzxgz5gLLdTDGOIwxfV2XRaWUUq5WaPA3\nxtiBD4BbgWbAAGNMswKWexX4zdWZVEop5VpuRVimI7BPRA4AGGO+AvoAO85b7p/APKDDpWYmPT2d\nyMhIUlJSLjWJq56Xlxe1a9fG3d29pLOilCrDihL8g4AjOd5HAp1yLmCMCQLuBnpSjOAfGRlJhQoV\nCAkJwRhzqclctUSE2NhYIiMjqVevXklnRylVhrnqgu/bwLMi4rzQQsaYYcaYdcaYdTExMXk+T0lJ\nITAwUAN/AYwxBAYG6pmRUqrYilLzPwrUyfG+9rl5ObUHvjoXtKsAtxljHCLyfc6FRGQGMAOgffv2\nkt/GNPBfmH4/SilXKErwXws0MsbUwwr69wMDcy4gIlltEMaYWcCC8wO/Ukqp0qPQ4C8iDmPMSGAh\nYAc+FZHtxpjHzn0+/TLnUSmllIsVpeaPiPwM/HzevHyDvog8XPxslRy73U7Lli1xOBw0bdqU2bNn\n4+Pjg5+fHwkJCfmuc+jQIVauXMnAgQPz/VwppUqbsn+H76pV8Mor1l8X8Pb2ZtOmTWzbtg0PDw+m\nTy/8xObQoUN88cUXLtm+UkpdCUWq+ZeIJ5+ETZsuvMyZM7BlCzidYLNBaCgEBBS8fOvW8PbbRc5C\n165d2bJlS6HLjRkzhp07d9K6dWseeughRo0aVeRtKKVUSSjbNf8zZ6zAD9bfM2dclrTD4eCXX36h\nZcuWhS47ZcoUunbtyqZNmzTwK6XKhNJb8y9KDX3VKrjhBkhLAw8PmDsXwsKKtdnk5GRat24NWDX/\nIUOGFCs9pZQqjUpv8C+KsDBYtAiWLoUePYod+CG7zV8ppa5mZTv4gxXwXRD0i6NChQrEx8eXaB6U\nUupilO02/1IiNDQUu91Oq1ateOutt0o6O0opVaiyX/N3sYL68hc0H8Dd3Z3FixdfriwppZTLac1f\nKaXKIa35X4StW7cyePDgXPM8PT2JiIgooRwppdSl0eB/EVq2bKk9gZRSVwVt9lFKqXJIg79SSpVD\nGvyVUqoc0uB/HrvdTuvWrWnRogX9+vUjKSnpotb//vvv2bHj/GfbK6VU6VKmg39iYiLPPfcclSpV\nYty4cRcdqPNzKUM656TBXylVFpTZ4L98+XLq1q3LO++8w+nTp3nrrbcIDg5m+fLlLttG165d2bdv\nHwCfffYZoaGhtGrVKk93z0wrV67kxx9/5JlnnqF169bs37/fZXlRSilXKrVdPZ988skLdqvcuXMn\nsbGxWe+Tk5NJTk6mX79+NG3aNN91WrduzdtFHM8/c0jnXr16sX37dl566SVWrlxJlSpVOHXqVL7r\ndOnShTvvvJPbb7+dvn37Fmk7SilVEspszf9yyRzSuX379gQHBzNkyBAWL15Mv379qFKlCgCVK1cu\n4VwqpVTxlNqaf2E19MGDB/P555/nmX/zzTczZ86cS96uDumslCoPymzNf+jQoQQGBuLt7Q1YQTsw\nMJChQ4e6fFvXX3893377bVYzU0HNPqDDOyulyoYyG/y7devG4cOHGTVqFBUrVmT06NEcPnyYbt26\nuXxbzZs3Z9y4cXTv3p1WrVoxevToApe9//77mTp1Km3atNELvkqpUsuISIlsuH379rJu3bpc83bu\n3FngxVqVTb8npcovY8x6EWlf3HTKbM1fKaXUpSu1F3xLu8mTJ/Ptt9/mmtevXz/GjRtXQjlSSqmi\n0+B/icaNG6eBXilVZmmzj1JKlUMa/JVSqhzS4K+UUuWQBv9S6uWXXy7pLCilrmIa/EuQiOB0OvP9\nTIO/UupyKtPBP2puFKtCVrHUtpRVIauImhtV7DQPHTpEixYtst6//vrrhIeH06NHD/79739nPehl\nzZo1AISHhzN48GDCwsJo1KgRH3/8cda6U6dOpUOHDoSGhjJhwoSs9Js0acKDDz5IixYtOHLkSJ48\njBkzJmuAuUGDBhV7n5RS6nxltqtn1Nwodg/bjTPJqjmn/p3K7mG7Aag+qPpl2WZSUhKbNm1i+fLl\nPPLII2zbtg2ALVu2sHr1ahITE2nTpg29e/dm27Zt7N27lzVr1iAi3HnnnSxfvpzg4GD27t3L7Nmz\n6dy5c77bmTJlCu+//74OMKeUumxKbfDf++ReEjYlFPj52dVnkdTcQ1M4k5zsGrKLYx8fy3cdv9Z+\nNHq70SXnacCAAYA1rtDZs2c5ffo0AH369MHb2xtvb2969uzJmjVr+PPPP/ntt99o06YNAAkJCezd\nu5fg4GDq1q1bYOBXSqkroUjB3xjTC3gHsAMzRWTKeZ8PAp4FDBAPjBCRzS7Oay7nB/7C5heVm5tb\nrnb4lJSUrNfGmFzLZr7Pb76IMHbsWIYPH57rs0OHDuHr61usPCqlVHEVGvyNMXbgA+AmIBJYa4z5\nUURyPqj2INBdROKMMbcCM4BOxclYYTX0VSGrSP07Nc98z7qetFna5pK3W716daKjo4mNjcXPz48F\nCxbQq1cvAL7++mt69uzJn3/+SUBAAAEBAQD88MMPjB07lsTERJYuXcqUKVPw9vbmhRdeYNCgQfj5\n+XH06FHc3d2LnA93d3fS09Mvah2llCqqotT8OwL7ROQAgDHmK6APkBX8RWRljuVXA7Vdmcn81J9c\nP1ebP4DNx0b9yfWLla67uzvjx4+nY8eOBAUFcc0112R95uXlRZs2bUhPT+fTTz/Nmh8aGkrPnj05\nefIkL7zwArVq1aJWrVrs3LmTsLAwAPz8/Pj888+x2+1FysewYcMIDQ2lbdu2zJ07t1j7pJRS5yt0\nSGdjTF+gl4g8eu79YKCTiIwsYPmngWsyly+IK4Z0jpobxYFxB0g9nIpnsCf1J9e/bBd7e/Toweuv\nv0779rlHUg0PD8fPz4+nn376smw3Pzqks1Lll6uGdHbpBV9jTE9gCHBdAZ8PA4YBBAcHF3t71QdV\nv2zBXimlrmZFCf5HgTo53tc+Ny8XY0woMBO4VURi80tIRGZgXQ+gffv2JfMUmUu0dOnSfOeHh4cX\nK91OnTqRmpr72sWcOXNo2bJlsdJVSqkLKUrwXws0MsbUwwr69wMDcy5gjAkG5gODRWSPy3N5FYuI\niCjpLCilyqFCg7+IOIwxI4GFWF09PxWR7caYx859Ph0YDwQC0851e3S4ok1KKaXU5VGkNn8R+Rn4\n+bx503O8fhS44AVepZRSpUeZHttHKaXUpdHgr5RS5ZAGf6WUKofKbPB/7TVYsiT3vCVLrPnFYbfb\ns4Zt7tevH0lJSRe1/vLly2nbti1ubm589913xcuMUkpdJmU2+HfoAPfdl30AWLLEet+hQ/HS9fb2\nZtOmTWzbtg0PDw+mT59e+Eo5BAcHM2vWLAYOHFj4wkopVUJK7ZDOTz4JhQ1nX6sW3HIL1KwJx49D\n06YwcaI15ad1a3j77aLnoWvXrmzZsgWAzz77jNdffx1jDKGhocyZMyffdUJCQgCw2crscVUpVQ6U\n2uBfFJUqWYH/8GEIDrbeu4rD4eCXX36hV69ebN++nZdeeomVK1dSpUoVTp065boNKaVUCSi1wb8o\nNd/5EuAAAAkBSURBVPTMpp4XXoAPP4QJE6Bnz+JtN/PxiWDV/IcMGcJHH31Ev379qFKlCgCVK1cu\n3kaUUqqEldrgX5jMwP/NN1bA79kz9/tLldnmr5RSV7My2zC9dm3uQN+zp/V+7VrXb+v666/n22+/\nJTbWGq9Om32UUmVdoeP5Xy6uGM//cvDz8yMhIe+zg2fPns3UqVOx2+20adOGWbNm5bv+2rVrufvu\nu4mLi8PLy4saNWqwfft2l+axNHxPSqmSUSrH878a5Bf4AR566CEeeuihQtfv0KEDkZGRrs6WUkq5\nVJlt9lFKKXXptOZ/iSZPnsy3336ba16/fv0YN25cCeVIKaWKToP/JRo3bpwGeqVUmaXNPkopVQ5p\n8FdKqXJIg79SSpVDGvyVUqocKvPB/3j8cbrP6s6JhBMuSa+44/mnpqbSv39/GjZsSKdOnTh06JBL\n8qWUUq5U5oP/pOWT+PPwn7y47EWXpFfc8fw/+eQTKlWqxL59+xg1ahTPPvusS/KllFL/3979h8Z9\n13Ecf766ZBzO0S0JbF0v024dwxakdqOsY11HFGkzSa0r6pC6aaFkLGIpIoXAyJ9OUfxBlxK1GGXY\nYe1sGZ26DcH902FXsm510/6g2oQ0ba+SOifWLG//+H7bndf7Xi7J99dx7wcc+ea+n8v3tXc+fe/u\n+737JE65favntt9uY+Rs9AJrr/79VaZt+ur3g4cHGTw8yAItYM3ta6o+ZsWtK/j+uvoX9J/Lev77\n9+9nYGAAgE2bNtHX14eZIanu4zrnXNJy2/xnsuq2VZz6xyku/PsC0zbNAi2g40Md3HnznbH8/Lmu\n5z82NkZnZycALS0tLFy4kFKpdHU5aOecy4PcNv96nqE/8cITDB0ZotBS4PL7l3nkY4/wzMPPzOu4\nvp6/c64Z5Lb512PiXxP03tPL1nu2MvT6EOPvjs/7Z853Pf/Fixdz5swZisUiU1NTTE5O0t7ePu9c\nzjkXp4Zu/vu+sO/q9s6HdyZ2nK6uLjZu3Mj27dtpb2/n4sWLkc/+e3p6GB4eZvXq1ezdu5euri4/\n3++cy52Gbv5pWb58Of39/axdu3bG9fy3bNnC5s2bWbp0KW1tbezZsyfdsM45Vwdv/hXmu55/oVC4\nZrVP55zLm4Z/n79zzrnZ82f+c+Tr+TvnGpk3/zny9fydc40sd6d9svqD8o3C6+Oci0Oumn+hUKBU\nKnmDi2BmlEolCoVC1lGccw0uV6d9isUio6OjnD9/PusouVUoFCgWi1nHcM41uLqav6R1wA+A64Cf\nmNm3KvYr3N8NvAc8bmZHZhumtbWVJUuWzPZhzjnnZmnG0z6SrgN2AuuBZcCjkpZVDFsP3BXetgKD\nMed0zjkXo3rO+a8CTpjZKTO7DOwBNlSM2QD83AKHgJskLYo5q3POuZjU0/wXA2fKvh8N75vtGOec\nczmR6gVfSVsJTgsB/EfSW2kef446gAtZh6iD54xXI+RshIzgOeN2dxw/pJ7mPwZ0ln1fDO+b7RjM\nbAgYApB02MzunVXaDHjOeHnO+DRCRvCccZN0OI6fU89pnz8Bd0laIul64IvAgYoxB4AvK3AfMGlm\n819c3znnXCJmfOZvZlOS+oDfEbzVc7eZHZPUG+7fBRwkeJvnCYK3en4lucjOOefmq65z/mZ2kKDB\nl9+3q2zbgCdneeyhWY7PiueMl+eMTyNkBM8Zt1hyypdScM655pOrtX2cc86lI/HmL2mdpL9IOiFp\nR5X9kvTDcP9RSSuTzlQlQ6ekP0j6s6Rjkr5eZcxDkiYljYS3p9LOGeY4LenNMMM1V/1zUs+7y+o0\nIumSpG0VYzKpp6Tdks6Vv81YUpuklyQdD7/eHPHYmnM54YzfkfRO+Dt9XtJNEY+tOT9SyDkgaazs\n99od8dhUalkj53NlGU9LGol4bJr1rNqHEpufZpbYjeAC8UngDuB64A1gWcWYbuBFQMB9wGtJZorI\nuQhYGW7fCPy1Ss6HgBfSzlYl62mgo8b+zOtZZQ6cBT6Sh3oCDwIrgbfK7vs2sCPc3gE8HfHfUXMu\nJ5zx00BLuP10tYz1zI8Ucg4A36hjTqRSy6icFfu/CzyVg3pW7UNJzc+kn/k3xNIQZjZu4UJ0ZvZP\n4G0a9xPKmdezwieBk2b2twwzXGVmfwQuVty9ARgOt4eBz1Z5aD1zObGMZvZ7M5sKvz1E8FmaTEXU\nsh6p1RJq55Qk4PPAL5M6fr1q9KFE5mfSzb/hloaQ9FHgE8BrVXbfH77sflHS8lSDfcCAlyW9ruAT\n05VyVU+Cz4VE/cPKQz0BbrEPPpdyFrilypg81fWrBK/uqplpfqTha+HvdXfEKYo81XINMGFmxyP2\nZ1LPij6UyPz0C75lJH0Y+DWwzcwuVew+AtxuZh8HfgT8Ju18oQfMbAXBSqpPSnowoxwzUvChwB7g\nV1V256We/8eC19C5fQucpH5gCng2YkjW82OQ4NTDCmCc4JRKnj1K7Wf9qdezVh+Kc34m3fxjWxoi\naZJaCQr+rJntq9xvZpfM7N1w+yDQKqkj5ZiY2Vj49RzwPMHLvXK5qGdoPXDEzCYqd+SlnqGJK6fG\nwq/nqozJvK6SHgc+A3wpbALXqGN+JMrMJszsfTObBn4ccfzMawkgqQX4HPBc1Ji06xnRhxKZn0k3\n/4ZYGiI87/dT4G0z+17EmFvDcUhaRVC7UnopQdINkm68sk1wEbBycbzM61km8llVHupZ5gDwWLj9\nGLC/yph65nJiFPxBpW8CPWb2XsSYeuZHoiquL22MOH6mtSzzKeAdMxuttjPtetboQ8nMzxSuYHcT\nXLU+CfSH9/UCveG2CP5YzEngTeDepDNVyfgAwUupo8BIeOuuyNkHHCO4in4IuD+DnHeEx38jzJLL\neoY5biBo5gvL7su8ngT/MxoH/ktwXnQL0A68AhwHXgbawrG3AQdrzeUUM54gOKd7ZX7uqswYNT9S\nzvmLcN4dJWg+i7KsZVTO8P6fXZmPZWOzrGdUH0pkfvonfJ1zrgn5BV/nnGtC3vydc64JefN3zrkm\n5M3fOeeakDd/55xrQt78nXOuCXnzd865JuTN3znnmtD/AC28huTj2OuPAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x118f6bf98>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18698)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "[1 0 1 1 1 0 1 0 1 0 0 0 0 0 0 0 1 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11948ea90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18709)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "28\n",
      "[1 1 1 1 1 1 1 0 1 1 0 1 0 0 0 1 1 0 0 0 1 1 1 0 0 0 0 0]\n",
      "stability stop at here 9 th problem\n",
      "similarity stop at here 10 th problem\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4VEX3xz93N71DgIChozQpoQmoVFFRFCSiIsWGIGJ9\nfUUpPzV0UFQEC2J9RSlSpIkiHRSkSe8CoaUAKaQnm93z+2NIIx022UDm8zz32ezu3Lnnbna/M3Pm\nzBlDRNBoNBpN+cLkaAM0Go1GU/po8ddoNJpyiBZ/jUajKYdo8ddoNJpyiBZ/jUajKYdo8ddoNJpy\niBZ/jUajKYdo8ddoNJpySKHibxjGt4ZhXDAM40A+7xuGYUw3DONfwzD2GYbR0v5majQajcaeOBWh\nzPfAp8AP+bz/AHDblaMt8MWVxwKpVKmS1K5du0hGajQajUaxa9euSyJS+XrrKVT8RWSTYRi1CyjS\nC/hBVJ6Ivw3D8DMMo5qIhBdUb+3atdm5c2exjNVoNJryjmEYp+1Rjz18/oHA2WzPz115TaPRaDRl\nlFKd8DUMY4hhGDsNw9h58eLF0ry0pqTZuhUmTVKPNzo3071oNPlQFJ9/YZwHamR7Xv3Ka7kQkVnA\nLIDWrVvrdKI3AwkJsHgxDB4MFgu4usK6ddC+vaMty5/kZDh7Fs6dU4/Zj6NH4eRJVc7JCd5/H557\nDnx9S9fGrVthwwbo3Llsf5aaGxZ7iP8y4GXDMOahJnovF+bv19xAiMDFi3DiRN5HZGTO8ikp8NBD\n0K8fPPCAEi8Pj9Kzd9MmWL4c6tQBH5+8BT4qKvd5lStDjRqq8TIMdd/p6fDGG/Df/0KzZnD33VlH\n9er2tfvyZTh4EA4dgtWrYeFCsNnAxQV+/RW6dbPv9TTlHqOwfP6GYcwFOgOVgEjgPcAZQERmGoZh\noKKBugNJwLMiUuhMbuvWrUVP+JYRRGDZMnVUqqReyy7w8fFZZQ1DCV+9elmH1QrjxyuxNJmgTRvY\nvVv1sF1doVMn1RA88ADUr6/qsAcWCxw4ALt2wc6dqqd89GjuchUqKGGvUUPZnvF39tfc3FTZrVvh\nnnsgLU0J75QpEBMDf/6p3ktIUOVq1YK77spqDG6/Xd17YcTGKoHPEPqMx/PZBstOTuqzzMBshl69\noG9f6NGjdBtTTZnDMIxdItL6uutx1GYuWvwdiAgcPw4bN6qe8h9/wIULWe87OeUU9+xHnTpZQpmd\nq90UKSmweTP89ps6jhxR5WrXzmoIunQBL6+i2ZyerkRy50517NoFe/dCaqp639cX/P3h1Cl1fyYT\nvPYajB1b9Gvkdy/Zbdi3TzUEf/2l7i/8yiDXzw/uvFM1BH5+asRRq5Zq6DJE/uBBCAvLqs/DAxo1\ngsaNVeNx++3q7/BwuPde1QA5OUHPnupaERHg6ZnVENx3n2pcNeUKLf6aomOzKQHKEPtNm5SQAFSp\noo6DB5Voms0wZgyMHm1fG0JD4fffVUOwdi0kJqqedYcOqiHo3l25PjZuVAJaoUJOod+zRzUoAN7e\n0KoVtG6d9Vi3LmzblrPXvnZtyfrLRdR9/fln1nHoUO5yGSKfIe4ZQl+rVv6jhasbIKtV/d/mzVMu\noeho1cgEB6uGoEsX1VBobnq0+Gvyx2pVveJNm5SYbt6c5ecODFRumE6doGNHaNAA/v67dEUzNVX1\nnDNGBQcPqtczfO3Z8fKCli2zRL51a7j11qKLZmnzzjswcaJqcE0mNWcwZUrRXEJFxWJR8wLz5sGS\nJcotV7kyPPaYagjuusu+19OUKbT4a7LYvBnmzlXimdETjYtT79Wtq0Q+Q+zr1Mnb5+5I0Tx7Vrlo\nfvlFPTcM6NNHjUDq11ejkRuFq+cMSrohTU5WDei8ebBihXoeGAhPPKFGGZGRalSgI4ZuGrT4a5Rf\nefRo+CFb5o1ateD++7PE3t5RKSVFaYtmSeKohjQ+XkU6zZsHK1eqESCAu/uN/XlqcqDFv7wiotw5\nM2aoIb/NluUqMZth3DgYOdKxNl4rjnbZ3Ey8+66KwMr4bowYoRauaW547CX+2jF4o5CUBF99Bc2b\nK3Fcv17Fny9cqHp2ZrPqMXfu7GhLr5327VXDpYX/+nngARWVleH7//prNWmu0VxBhweUdU6ehM8/\nh2++UTHizZurH/KTT2bFe69dq3vMmpy0b5/1vQgMVO7Bu++G+fPVWgFNuUe7fcoiIiqa49NP1SSe\nyQSPPgqvvKIiOey1SEpTfggLg4cfVr3/adPUd0lzQ2Ivt4/u+Zcl4uPhf/9Ton/0qIq/Hz0ahg5V\nvTeN5lq55RY1V9SvH7z6Kvz7L3z00Y0VSaWxK1r8Hc3WrbBggQp3XLVKNQB33AGzZ6u4bb2CU2Mv\nPD1VEr7hw+Hjj5VLce7c4q+A1twUaPF3JKtXq4m5jJC87t1VbPsddzjWLs3Ni9msevy33QYvv6zC\ngZcv1yPLcoiO9nEUmzfD449nCb/ZrH6IWvg1pcGLL6r5pOPHoW1bHQlUDtHiX9pYLPB//6ciczw9\nlVvnZgjT1Nx4PPCAWg1uGCoSaMUKR1tUcugNenKh3T6lyfHj0L8/7NihNgiZNk2lJNZhmhpH0by5\nSoj38MMqW+jNGAm0fr3KgGqzqc6WXu0MaPEvHURUnP5rr6kv38KFKnQT1JdQfxE1jiQjEqh/fxUJ\ndPy4mhC+GSKBzp+HAQOy9kdIS1OdLf2b026fEufSJZV2d/Bg9YXbty9L+DWasoKnJyxapLKQzpgB\njzyStXHNjcr27WpjoZiYrHTXhqHdq1fQ4l+S/PGH2v5v5UqYOlU9v1ESrWnKH2YzfPihWlG+cqXa\na2HZshvTVz5njgqgcHVVjcCmTSoduM2md0LLQEQccrRq1UpuWpKTRV5/XQREGjcW2bPH0RZpNMVj\n5UoRd3f1HTaZ1N9btjjaqsKxWkVGjlR2d+wocuFC1ntRUSKVK4u0a6fK3aAAO8UOGqx7/vZm/34V\nrjltmoqj3rlTTappNDcSDzygXJWgessZvvKyTHy8crFOmgTPP6/W0VSunPV+xYpqZPP33zBrluPs\nLCNo8bcXNht88onyMV64oIbNM2aojJsazY1I375ZvnInp7LtKw8NVXmvli9Xv8NZs1T49NUMGABd\nu6oU1xlbmZZTtPjbg/BwePBBeP11FVK2b5/qOWk0NzLt26tdwjw91R7E7do52qK82bxZdbrOnlX7\nRL/6av7JDw0DvvhC7Xj2n/+Urp1lDC3+18PWrTBwIDRsqCaUvvgCli5VCdk0mpuBbt3UHsR79qjR\nbFnjm2/UDnAVK6r1CvfeW/g59evDqFFqx7NVq0rexjKKTul8rWzdqrZKtFhUb+Knn1SOfY3mZsNi\nUfsBu7mpRqAsxP+np6sEddOmqdH2vHlQoULRz09NVZF46elqoeUN5J7VO3k5mmnT1I8CVL790FCH\nmqPRlBjOzjBxohLJ7PtFO4rYWHjoIfUbfO01+PXX4gk/qBDQmTNVZtPx40vGzjKOFv9rYdUqtSDG\nZNJ5eTTlgz59VBTbu+8qf7mjOHZMzT2sW6e2NZ02LWtSurh06QJPPQUffAAHD9rXzhsALf7FZft2\ntUK3SRM1uTRunM4Vorn5MQx4/304dw6mT3eMDatXqwykUVGwZo0K57xepk4Fb2+1YZLNdv313UBo\n8S8OR4+q/U+rVFFREPfeqzcc15QfOnVS3/9Jk5QAlxZbtkDPnmq/i+rVVWLEjh3tU3flyqrn/+ef\n8N139qnzBkGLf1EJC4P771c9oFWroFo1R1uk0ZQ+kyerxVQTJ5bO9TICK5YvV88//hhq17bvNZ59\nVqWyGD5crdEpJ2jxLwqxsarXERWlevy33eZoizQax9CkCTzzjNpnujSCHD78MCsjp2GoXr+9MQw1\n+ZuQAG++af/6yyha/AsjOVkNOY8cUfuftmrlaIs0GscyZowKdnjnnZK9zvbtat1MaQRWNG4Mb72l\n9s5et65krlHG0OJfEFYr9OunVhD+8EPRFpBoNDc71aur1ew//VRy2z9GRKg8PdWrq1DO0gisGD0a\n6tVTW1ympJTcdcoIWvzzQwSGDYMlS1SukL59HW2RRlN2ePttFVv/9tv2rzstDR57DKKj4ZdflMu1\nNAIr3N1VOutjx9Tcxk2OFv/8CAlRyaFGjlS5QjQaTRZ+fmov6j/+UGGX9uSNN1T0zTffQFCQfesu\njPvuUyv1J01S0X03MVr88+Lzz2HsWLXP7oQJjrZGoymbDBumIm/eest+MfLffQeffQb//a/j0qV8\n9JEaBbz4ovIA3KQUSfwNw+huGMZRwzD+NQxjRB7v+xqGsdwwjL2GYRw0DONZ+5taSixcqPLwP/ww\nfPll/tkBNZryjqurSo2wezfMnXv99W3frhZb3XOPY90uVauq669fryaAb1IKTexmGIYZOAbcC5wD\ndgBPisihbGVGAb4i8rZhGJWBo0BVEUnLr94ymdht/XrlX2zTRg1n9XZvGk3B2Gxqe8SYGBUR5+p6\nbfVERKh6nJ3VBkj+/va1s7jYbGp/gH//VfflaHuyYa/EbkVJinEH8K+InLxy4XlAL+BQtjICeBuG\nYQBeQDSQfr3GlSq7d0OvXiqGf9kyLfxF5P33od7laG756SipZ1JxrelKWP8GnPCtyFtvFb2eyJ8i\nOTn6ZGYddSfUJaB/QKna8f77UONSBH5f7sY1zpVUn1Quv9CSM5UCilxHWbgPe9hR5DpMJpXy+b77\nlLs0W478ItuQfYJ3y5YcQltq93E1JpMa+bdsCW+/TWSXCddlhz3voz717RJvXhTxDwTOZnt+Dmh7\nVZlPgWVAGOANPCEiN06ijJMn1eYrfn4qX0/Fio62qEiUBcGrdzmaQRO9eA93WpDK36fdGTPRi29G\nRQNF+xzfeTIe/18iCEpNBSD1dCpzB0UQtcKDcXO9i1TH9dohIjjv28vQnxoTQjVaEMuRuGqM+aAC\n7/Xbgy21GZmjZLlyXPk74/WQZ5KotDyCoLRs9/FcBBcXuxHyjUfWOZDv37XDYxk0zTfXfcx6/SJp\nF31znkfedYwZmkzllXnYsdCVd78oWurii4svcvLNk9iSbZl1HB18FEushcrBlXMWbtIROj4CYz+F\nBwaAr486f3ge58fkcf6oUfDnYfjsO6jSCMJSs2x4q4h1FHQf11pHpQYwZCQXv9jPyR8PY1NmFduO\nkrgPe1AUt08foLuIPH/l+UCgrYi8fFWZu4A3gHrAaqC5iMRdVdcQYAhAzZo1W50+fdpuN3LNREaq\n4V1MjIowaNTI0RYVmUWjMwTvEC2IZTd+jKEx34xK4NEJRRPej/rvYcycxvyXYzQgnr34MYNbGXzn\nSbo/FUhKopCSJKQmQUqykJIspCajjlQh/PcYzqR5sMErHac+T5C+8GceTBAaOMcT2MEbLycrniYr\nXiYrHkY6XoYVF7Eh6ZJ5bNxiIsTSKNd9hJgOc2f9VMQmYCPHY7rVINlqkGw1kWI1kRBtYx++fENd\n2hLF3/jzKGepQQpWZxPpYsICWGwm0jGwiEG6qL/TMbCg/o7EjT1eyTj1eQLrwvl0SjBThyTcsRZ6\nHMKbcTTOdR/vcYhmxJKG6cphJvXK31c/pmHmOF4s5RaaEct+/HiKUFoSizcWfEjHHSsFzURlv+7V\ndrQg1i7fPY3jeIEXOCpHr3sysiji3x4IEZH7rzwfCSAik7KV+RWYLCKbrzxfB4wQke351VsmfP5r\n1sDTT8OlS7BxY6luU3e9vXabTVhXcwfrz3vzCbdyB9Fsw58ehBHgno5zuwrExxskJKGOFBOJKQaJ\nFhNJFhOJ6SaSbWaSMGMrUEqKSI8XodUs2PUC/Pp5gUWdDRueZiteTla8nGy4JaRiwcQxvKlJImfw\npD7xuGLD6u+qBD79itCnm0i2GqTbrj9QzcCGifTMwwtXnBCierxOequvMHYNwfj182J9Ps5YSceE\nB1aSMOOK7UoDU0x7vcKhT19YOB8SquZ4y8kk+HrY8PO04edhw9dD8Mt47mkj7ddIonFhKYF0I4IN\nVMkU/tu+KFpqkuMvHs/3vfoz6+f9xrffwj//wLhxHBt5Mf/zv7xy/qlQ+OB95Wp99TUw5/yMjr1w\nrPA6CsE+dRyFfL4DRanD3vdRmuLvhJrwvQc4j5rw7SciB7OV+QKIFJEQwzACgH9QPf9L+dXrcPHf\nvFktFbfZ1LLxDRtKNTtnfr32qUPiaXqfO+EnrYSH2ogIs3EhAi5EGVy8bCIq0UxUihMxVifSKHhH\nJVesuBtW3Ix03AwLrkYazkYKZpIxbImI9TJNqI0HNvbiy3b8uYuLdOMCTliZzdeYndNxcRNcPARX\nT3DzNHD3NuHha8bT14nZ9T7F4mzJdW0nizM15jxEZGQKSUlmwPfK4QP44uTkj7t7AM7OlQmIvpVU\n3InElUSc8cZCACmYSSWq0llstngsljgslhhSU2MQSQCSrhyJQBL/4QUSqcZsL2fc+jxB6sKfeSkh\ngepGKIs6zMXT0xlPTxe8vV1x9zJjeFmwelpIdUklyTmJBFMCv0X9hs2Ue1jtnO5Mz8PPERmZyMWL\nyURFpRIdnYbN5o6a4lLHozyDGV9248dxvLmNeJpxGQtxLGM+kAIkZ3tUfzs5pePl5YS3txOvnxtK\ntNTk/R7TsbSahdOuIQz59b+4EcFHfIdyYVXEySkAD49AXFwCMAx/0tN9SEnxJDnZOZf9ZmzcSgL1\n3BK5f0o1mjVTm1gV5N3cWnsrqadTc3+narnSPjSf30loKDRoAAMGsHXt8wWfX4QJ3muy4SpeqnCa\nW2Njcox4duPHv34V+CymVpHquF477HEf2euwl/gX6vMXkXTDMF4GVgFm4FsROWgYxtAr788ExgHf\nG4axH9VEvl2Q8JcJJk3Kik22WktF/G02IeywhX3rU9k+LYoWWHiLZniRzmXUj3bwLH+YlfM8MzYq\nmCz4u6bj72Gltn8ynq4JeP+bTLJ4sIxAOnKBP6nM6xyjDscYxBOkYiH1in/a3d2dgIAAfKtUISAg\ngICAAKpUqULrqa0JTavDPC8zVfo8yL6FC3g0wcKtHmcZFT8NQTgXd47Q2FBOxZ7iVMwpTsWeynxu\nuZxb+EnxomZEU6as6Efn2p1xs7kRHh5OWFgY58+fJywsjLCww4SFreX8+fP47vClQ+q7TCKIgYSy\njFt4nsNsdA7BvXo4/v7+VKxYMcejv39gtr/9GfvIZlYcuYNmnfuyvdY2Gj00gBmHn6LhrSu589mq\nnI8/T2j8ec7FnSMyIRJBVEjClbAEF7MLPtYqxCalY3hEI9kaAYs5nePdttIusB3tqnelbfW2NPBv\nQHxcPBcuXODixYtcuHCB39+cQd2Tg1hFQOZ9tCGM47Vm8dX/tcPX1xc/Pz98fX1z/O3q6kpEQgRH\no47S9btuiMmaee30Nl/yeZsvMVmd2R28ndDQUEJDQzl9+gChoSsyn8fGZoibmQ705A7e40sa04xY\ndlKRdIQN6T6sfC3r31S9OpkNQfPm6rF+fbU3ysr2TfCPOElQakxm+T2uFYhqX5d8fyW1a6sw6WnT\nqDtxGEfHWrAlZX2OJg8TdSfULXCCNzt1J9Tl6JCjeddRRLoO82bQxGqM5DBtiGFvhmt0WEKR66g7\noS5Hn9mPLT1LLk3O1iLbYY/7yKuO66V87uF74oRK5JSeruL4XVyKlTekMJdNcpyNgxtSOPCXhUN7\nbBw/YXAi0onQRFfiJatn5owNT9KJxYVbiac9UVTAQpPXq1CxmoV05zBibUc5G72fY8ePcPToUY4d\nO0bKlbwjLXiDE0wiJNvoIYTGdKw2g0Gf18sU+ICAADw9PTHyWLPw8cA9vDevLrWDe3Og0Xqqn7+d\niAttqN3wINbAS5y5fIZ0W1bgloFBoE8gdfzqUKdCHQ7+6cfx6CXE1zyL2Wom3ZyOS5wP4m3FYkoE\nIKhqEF1rd+WeuvfQoWYHvF1zTuLee+9ENq4Zxtv8QxdgPTCFlnTq9jmrV4/K838gIpyNO8veiL3s\njdzLu+veQ4y8fxgV3CoQ6BNIoLc6qvtUz3ruo577u/vzwQcGq+OeZq15Ns5WZyxOFppbu+LvdhdO\ntbax7fw2YlOUyPq4+tA2sC1tA9vSrno72lZvy5O9ZrFxzTCGea1mfZ/xdF34Dp8ldMu8j2RLMsej\nj3P00lGORqnjyKUjHIs6Rlxqtukxm4EBiClrgrmypRnDHxxAr4a9qO+f21Vw+fJlTp8+TWhoKMOH\nr+TUscm5Pk/fSkMYNSqYW27pzpkzfuzbB3v3wuHDWYkzXV3h9ttVmvvNG2wM8jhNr5gzHA6oxHtJ\njVi41ESXLnl+zIqoKJUfp0MHIvt+nXeEy0svqcigOXMKXchV3CgZm039vDPube9e2LrRysXLZjxJ\nJ90w+GBIAq/M9C3wurnsCNnMyTHnSaUKrsZF6r57CwEhHYp+vh2jfZ45/UzpuH1KCoeJvwh066aG\nmrNnq+3bOncuVq8/w2UzksO4Y2Mz/iwnkFs9kolNcyI83TWHn9jflEZdn1Tq3WKlfn2hcQsz1mmH\nCI9xYxyN6UkYy7iF9zhELeeTvFzpZcLDwzPPN5lM1K1blwYNGuQ4Xn75DM4H9vAWbahCFS5wgffZ\nAS3a8M8/eeciEhFOxJxg27lt/H3ubz7d8Rl5h5AY9G3yhBJ5vzrU9qtNnQp1qOlbExezS46Sveb0\n4tyRcxyfe5z6T9YnsGEgC59YyM6wnaw9tZZ1p9bx19m/SLOm4WRy4o7AO7inzj10rdOV9tXb89rL\n55k79w3SXFaR8lAKbivccEm7nyef/IiZM+uSbEnm4MWDmUK/N3Iv+yL3ZQoxQE3fmlitVsLjw7EZ\nNsxi5v5b72dGjxnUrVD0Hlbw/GCqeVVjSKshzNo1i/CEcBY/sRgAm9g4FnUs87Pbdn4b+yL3YRXV\nU/dJr0nycRvpXueR6oJx1sD5Uh2qNA7EqepZTseeViOOK9TwqUGDSg1o4N+AhpUa0sC/AQ0qNWDs\n+rF8u+dbxCpghpZVWyKGsDtiNwANKzWkV4NePNLwEe4IvAOTkdNX3rLlPHbv/hLYkO3Vzjg734XF\nMgGTycRdd91Fr1696NWrFzVr3sqRI0oo9+3LOiIi1Jne3kpUf/wRHnmkCB/ilCkwYoSaR7t605Vv\nv4VBg9QK3qlTC6zm/ffVkpvsjc369Sqr81tvQVwc7N+fZffevep5oupzYDIpL1Tjxuls336Gs2fr\nor7rBm3bqh0cn3iiGCH8//d/asX/l1/CkCFFPMn+2CvOv/yJ/zffqO3fZs6EF14o1qnJcTbW/S+R\nhcOj2ZTqz0k8yZgIcsJKLZK5tbqV22rbaNTURON2Zpp1daNi9azhYkxMDBs3buSz5w6xPebNXL32\npm7juK3vuRwiX69ePVzzWDyzadMmgoODSTQSM0XTUzxZvHgxHa/86GJTYtl+frsSrPN/s+3cNqKS\n1S5Mns6eNAtoRmxKLCdiTpBmTcPdyZ3gRsFMvW8qVb2q5rrmtZJsSWbL2S2ZjcGOsB3YxIabkxt3\n17ybDtU7MGf9HI6ajlJP6jGw00AORx9mb+RejkUdw3YlctjT2ZOmAU1pHtCc5gHNaRbQjKYBTfFx\n9eHFFS8y659ZuJhdSLOm8UKrF/i8R8GTz9dLYloiu8J3se3cNkasHZFpZ3YMDPo26Zsp7g0rNeS2\nirfh6eKZZ535NUCnY0+z7Ogylh5dysbTG0m3pVPVqyoP13+YXg16cU/de3Bzcsv3e7Fo0SK8vb1Z\nunQpS5YsYd++fQA0btyYRx55hF69etG6dWtMJtWYnDqVyIMPnuTIkaYAuLkJ/fsbvPKKchPlS3Ky\n8h8FBqrNWDJGnNu2qcagQwcVUl3I3rvr18Pjj8O8ecqj9NNPql1p0ULtrXTqVFZZPz9lU8bRrJka\nwezYsYmHH/6IuLivgC+Al3B1XUb16o9x4oQXzs5qc7KBA9VjgWvU4uMhIEAFiXzxRYG2lyRa/K+F\nsDDl7gkKUjm7TQVHYKQm2dj0YxKrF1rYtMuJPdEepGLGhHAb8ZgRDuFLMOd4iX8xGdDZ1jlHHfHx\n8WzevJn169ezbt06du/ejYhgGG8TJJZi9drzIikpiQ6TO/CP6R9a2Frw2bOfsTdqL9vOb2PbuW0c\nvnQYUALUqHIj2gUqF0W76u1oXLkxTiYnh4jm5ZTLbDq9ibWn1jJ92/QcPeIMDAx6NuhJs4BmSuyr\nNqduhbq5eroZFNRrLw3C48N58483WXxkMSnpKbg5uRHcKJgP7/vQrg0pQExyDL/9+xtLjy5l5fGV\nJKQl4OnsSfdbu9OrQS+6VO9Cr0968Y/pH1raWrJ5xGY8rlq4eOrUKZYtW8bSpUvZtGkTVquVatWq\n0bNnT+rVq8e4cX8SH/81SjRfwdl5GybTvaSmmunUSeU77NkzHw3/7juVG2vBArX5ezFX8CYkqGC8\nWbNUO5Fdpho0yJqnyDiqV8+diSU8PJyuXcdx5MgY4HHUSKgz8DPdun3F1KmjmD1bNSoRESpJ6RNP\nqBFBu3b5ZHbp108ZFBGh3MUOQIt/cRFR+cF//533XzlLPWdTLp/9cc8K3FkziVXz0ti03cyuix4k\nX5kTv9UtibsaptLtQTNVvj/CwTAXxlzlsmlXK5nmh5qzZcuWTLHfsWMHVqsVFxcX2rdvT5cuXeja\ntStffPEFc/PIhzJgwABmFzGfiPsEd1LS8847XsmjkvJFX/FJt7mlDb5uefs5y4JovrzyZVYcX0Ga\nNQ1Xsys9G/Rk+gPT7S6aJY0jGtLU9FTWh65n6ZGlLDu2jLD4sDzLuTm5kTw6Oc/3oqOjWblyJUuW\nLOH3338nMbEN8DNXi2anTrN56KE3+PRTOH0aatZULvznn78qeshqVZ2s1FSV8//++2HXLjXBm0+m\nztOnYcUKtWPj+vVqXtjXVw0gDh1SbcmMGfkvvhcR/vnnH1asWMGKFStQ+jIcFaC4IVvJzjRtOoh9\n+wYAar5j7VrlBV68WA1cbr1VjQYGDFDpvjLdTytXQo8erB+7mR2udxdr1bW9sJf4IyIOOVq1aiWl\nyoIFIiBqt5XQAAAgAElEQVQyZYosHBUlvqTKVHbLN2yXRzgrTljFDYuoVkKktkuSPHl7jHz9Rqyc\nO5Sao6qPBuwWH1LlI3bLetbLR6jn3StPERcXFwHEbDZL+/btZdSoUbJmzRpJSkrKUcfGjRvF399f\n3N3dBRB3d3fx9/eXjRs3FnorJ6NPysRNE6XhjIZCCJmH01gnufObO+Xvs3+LzWaz68dX0gxdPlRM\nY0ziNt5NTGNM8uKKFx1t0jXRe15vGbZimOwJ3yPDVgyT3vN6l+r1rTarrDy2Um7/7HYxQgwhBDGP\nMUuf+X0kPD68SHUkJydL3bozBTpnrGW+cnSWFi3miohIerrIL7+IdO6sfi/u7iKDB4vs25etohUr\n1JtVqqjHOXNyXCc9XWTLFpGRI0WaNpXM395tt4m88YbIunUif/whUqmSyDvvqMd163LampiYKMuW\nLZPBgwfLLbfcIoAYhiHt27eXCRMmSI8ePa66B3W4uLjIZ599JhaLJUd9cXEi330n0rWriGEoe5o0\nEfHyElm6VEQsFlnn11squcTmsqW0AHaKHTS4fIh/VJRIQIBIy5YiFovMr7JLuhIhBrbML5w/KdLD\nKUw+HxYrJ3elFFhdUNAcacEbMpe5spa1Mpe50oI3xN39PXnzzTdl5cqVEhcXV6hZiYmJMmrUKPHz\n85PRo0dLYmJivmXD4sJk2tZp0vartplif+c3d8pd39wlRoihRVOTg4zG1GmskxCCuI93l8WHFhf5\n/AEDBuQpmg899FCusnv3ijz/vIibm/otdeki8tRTIqun7RcxmdSLTk6ybsYBGTNG9cOeekqJOYiY\nzaoRmTpV5OjRrHrXrRPx97dJv35fiZ+fn/Tr95VUqmSTefMi5YsvvpAePXqIm5ubAOLt7S19+vSR\n77//Xi5cuJBZR16dLF9fX2nRooUA0rRpU1mXj4qfOSMyaZJIo0ZZDdP994tUcouXdU73ikRHF/0f\nYke0+BeHZ56RZMNLZvU7LndUiBMQMWGT6iQKiARzVtazXtYb6wus5tKlS/Lxxx+Lj49Pnj+MAQMG\n2NXsqKQombVzlnT5vktmLy5oZpBM3jxZTsWcEhEtmpq8yf69ePznx8V3kq8QgvRd2FcuJl4s9Pyr\nRdPFxUUMwxCz2Szjx4+XtLS0XOdcuiQyebJIjRpKWUyGVV7gC9lLU3mJT8XZlC5ms3qvQgWR/v1F\n5s4ViYnJ24YXXjghPj69xMPDQwBxcnISk+kegeECSN26deW1116T1atXS2pqat6VSN6dLJvNJosW\nLZLatWsLIMHBwXLy5Mk8z7fZRHbuVCMSEGlYK0li8BX58stCP8eSQIt/Edk76U8ZzCrxI1VAJMCc\nIoNcT8m77BdfUmUgp8T3igtnS60tuc632WyyYcMG6devn7i6ugog/v7+dhP/sLgw6fhdx8wheXxq\nvPy490fp8VOPzF7bbdNvk3fXvSuHLhy67s9DUz5JS0+TcRvHifNYZ6nyQRVZdGhRoedcLZqhoaHy\n+OOPCyAtW7aUfTl8PFlYLCILF4o0q5eQ2WMGkZoByTJ8uMimTapMYfTt2zfP31mLFi3k0KFDdnFt\nJiUlyfjx48XDw0NcXV1l9OjRkpCQkKvcunVqpHLvvSJgk6rmSNkeNPi6r38taPEvgKTLVpn5Soy0\n8b18pZdvlXuqxsj8cZfFkmrL9Pln99n7kioLR0Vl1nHx4kWZOnWqNGjQQADx8fGRl156Sfbs2XNd\n/vqreXHFi2IaY5L7Z98vjy94XNzHuwshSI2Pasibq96UXWG7bjj/vabssi9in7T8sqUQgjyx4Am5\nkHCh8JOuYsGCBVK5cmVxdnaWcePG5TkKyGBwz3ABkZcfDSty/WFhYTJq1KjM+bOSHmGLiJw7d076\n9+8vgAQGBsqPP/6Y+bvLEP4M79Cnn6pRjZk0mfbuJSntn2e5Ff8pU0QWjoqSLbW2yHpjvWyptUUW\njoqSKVNEdq9MkkGtosXXSBMQqWYkynDmy6mvt+aqY87wcFnps1LWslZW+qyUucMjZPJkm6xbt076\n9u2b+cW788475fvvv8/ljy+Ovz4v3Ma75ZiszTjMY8yy+fRmsdqs1/T5aDSFkZaeJhM2TRDnsc5S\n+f3KsuDggmLXcfHixcyeeYsWLWTv3r25ymSIZn6TtVdz4MABefbZZzNdTDVq1CixEXZ+/PXXX9K6\ndevM3/6OHTtkyhSRX39NkpEjR4qfn5+MGjVK5s84LY04ICDyyCOl6/4vt+J/da99CnvEA4vc6qKG\nmGascm9grCwcekDSMYsMG5arjoyee4Yv0d3dXTw8PKR69eoCiJ+fn7z66quyf//+a7KxMCLiI2Tw\nssFiGmPKFH3Xca7y5MInixyRodFcL/sj90vrWa2FEOSxnx+TyITIYtexaNEiqVKlijg5OcmYMWMy\nRwFX95avfp6BzWaTNWvWSPfu3TN/iy+99JIcP368REbYRQmIsFqt8u2330pAQIAYhiEPPvigVKhQ\nIYde+Pv7y4amzeSjyhPFyckmtWqJbNtWbLOuiXIr/ltqbZGP2C3epEkjYjMjdm4hSUZ0j5Yz+1NF\nUlNFbr9dpHp1kcuXc9WRXyRD5cqV5YcffsgVlmkvIhMi5b+r/ivu493FPMYs9WfUvykidTQ3Lhar\nRSZumigu41yk0vuVZP6B+cWu49KlS9KvXz8BJCgoSHbv3i1TpuQW+nXr1KhbRCQtLU1mz54tQUFB\nAkhAQICMHz9eLl26lOOckhphu413K/Tcy5cvy/Dhw8UwjLxHIG3bioD8/e1BqVVLxMlJ5KOPpMTd\nQOVW/H/hT+nFOeGK6NciQaayW9ayPqtQSIi6tRUr8qwjv9jfkvAliohcTLwob/3xlnhM8BDTGJMM\nXDxQjl06piN1NGWGA5EHpM2sNkII0ufnPpmjgKK6S0REfvnlFwkICBAnJyd57733JDo6OoerJDEx\nUWJjY+X999/PHGU3atRIvv76a0lOTi6R+1pzYo1UnVo1U/Tdx7tL/0X9izXC7tmzZ9568fjjIq6u\nIq++KtHRIr16Kdnp2VNFl5cU5U78E2LSZUT3aPHEIgY2cSFd+nAmd6TOgQMizs4i/frlqiMsLEwG\nDRqU5z+yJMT/UuIlGblmpHhO8BQjxJD+i/rLkYtH7HoNjcZeWKwWmbx5sriMcxH/Kf4yb/+8zPUC\nRR2VXrp0KXPi1Gw2Z8bhu7m5iZubW6YLp2vXrvLrr7+K1Voyc1vn487Lc0ueEyPEEJdxLjl6/U//\n8nSx6srPUzBgwACRPn1EKlcWSUsTm01k2jQlPzVrimzdWnjd10K5EX+r1SZfvBwrVc3JAiJNPOLF\nm7S8I3XS00XatlXOxWwLPRISEmTMmDHi6ekpzs7O8thjj0nFihXt4kvMi6ikKBm9drR4T/QWI8SQ\nvgv76jBNzQ3DwQsHM9eVXIu7RESkU6dOeQpm7dq1ZdeuXSVme0Jqgry3/j3xmOAhzmOd5Y3f35Ae\nP/WQYSuGyde7vhbzGLN4T/SWiPiIItd59dxDxrqHdevWqWW/V3kZtm0TqV1buYE+/ND+bqByIf6/\nfx4vt3uqidz67omyZGpcgdE+8vHH6pZ++klE1MTNd999l7ns+9FHH5Xjx4+LyPX7EkVyD4ljkmPk\n3XXvis8kn8xJtAORB4pdr0bjaM7EnpGgL4Ku2V1SYG+5BEi3psvXu76WalOrZf72/o36N1e5Dac2\niMcED2n0aaNiNQDZ9aJLly4CSN++fcWSmCji7y/St2+O8tHRKgoI1ArhJUty1pd9/qO43NTif2Bt\nstwbGCsgUsmUIh8PihFLaiHN58mTIh4eIg8+KGKzydq1azMnk+644w7ZvHlzYZ9pscmIIHhuyXMS\nsj4kcxXlo/MflX0ReS+A0WhuFIYuH5pjBDBk2ZAin1ua4r/q31XS9POmQgjS7ut28teZvwosn70B\nuNbouilTpmQ1AEOHqtwWVwWX2Gwin3yi0leYTCIzZqjX84t8Kio3pfhHnkyTZ1vGiBNWcSdd/tMl\nWi5Hphf+adhsIt26iXh5yeF16+Shhx4SQGrVqiVz5syxu18xvwgC0xiT7AnfY9draTSOIiMgYeyG\nsUIIUm1qNbFYi7A0V64vcWFR2R+5X7r/2F0IQepMqyM/H/i5yAsiN4ZuFI8JHtLw04bX3AC8//77\nAsgT3bqJBUS+/TbPctu3q9RioFYIX4/wi9xk4p8cb5V3H4kWbyNNDGzyaP0YObW74ORqOfjuO7kA\nMqxjRzGbzeLj4yOTJ08usQiCsLgw6fp91xxZEx/48QEdo6+5aZn+93QhBBm4eGCRFyDaw7WaF+Hx\n4ZnrZPwm+8mHWz6UFEsx9OIKG0M3iucET/s0AF5eYuncOd9yMTEiDRpkNQDXww0v/vWpL3/V2CKv\ntL0ovia1IrddxTjZuqDwL0hCQkJmCNlbL70kY93cxMdsFrPZLMOGDcuR1c/eRCdFy3NLnsvs7buM\nc9Ex+ppywbiN44QQ5KVfXyrVlCMZc2snok7I2A1jxXOCpziPdZbXf3tdopKuL6ZyU+imzAYgLK7o\nKSiy88EHHwggj4NYTp3Ks0yGqycjQ+ioUddu8w0v/jVpJHWJFxAJMCXLvDGXxWot/At19epc44ov\nsX1QkBw6VLIRNQsPLpSqU6uqBVrT68uQZUN0jL6m3GCz2eTNVW8KIcioNdehXsXkheUviBFiiMcE\nj8w5teNRx+1Wvz0agKkjRqgGoFmzXHsEZPfxp6SIBAUp5S23E77QSgxs0ofTsqlG7mya+VHaUQQi\nqucRPD9YCEFazGwh/4T9U2LX0mjKMjabTYYsGyKEIFP+vEb1KiLXszq3uGQ0AA1mNLj2BqBWLQHk\nsccey5Hs7urVzrGxIvXqqfUA15ISwl7iX/AmtiXME5zlJU5iPZdapPI2m40jR46UsFVZiAjf/PMN\njT9vzMrjK5l8z2S2D95Oi2otSs0GjaYsYRgGn/f4nL5N+vL2mreZuXNmiVzHarMy/M7hmLJJlIeT\nB/2b9ufUa6fsfr0OtTrw+4DfORd3ji7/60J4fHix6/jv22/zIbBgwQL69++PxWIB4K23rmwBeQVf\nX/jzT7XvcI8ecOyYnW6imDhM/CuSxm9UZTd+uNZ0LbR8aGgo3bp1o7T2/T0RfYJus7vx/PLnaR7Q\nnH1D9/H23W/jZMprt2qNpvxgNpn54ZEfeKj+Qwz7dRhz9s+xa/3Ho47T6ftOjNs0jpp+NTEwcHNy\nI8Wago+rT4nt63x3zbv5fcDvnI8/T+f/dS5+A/D447zh7MxHnTuzYMEC+vXrl9kAXE3VqrBqldok\n/r77ICzvbZdLFnsMH67lqE/9PPPoX43NZpOvvvpKvLy8xMvLS4YPHy7+bm7ifsXd4w7i7+FhtxAy\ni9UiU/+aKu7j3cVnko98ufNLnV5Zo8mDpLQk6fx9ZzGPMcvSI0uvuz6rzSrT/54u7uPdxW+yn8ze\nO9sh+a/+PP2neE30kvoz6sv5uPPFO7lXL5Fq1eSjqVMFkD59+hS438HOnWp/4GbN8t/R7Gq40X3+\n9amfc3VuHpw/f14eeOABAaRLly5y6spMeuKdd8ooED+Q0U5OkminnZT3RuzNTHHbc25POXf5nF3q\n1WhuVuJS4qTNrDbiOs5V1pxYc831nIo5JV2+7yKEIA/8+IDDf3vX3AAsXKhkddUq+eijjzIzCxTU\nAPzxh/L/d+woUpTo9Bte/Ata4Wuz2eTHH38UPz8/cXd3l+nTp2ct1NqyRZn9wgsiEyeq59dIRghZ\naEyo/N/a/xOnsU5S5YMqMv/AfL17lkZTRC4lXpImnzcRzwmesvVs8bKZ2Ww2+XLnl+I10Uu8J3rL\n17u+LjO/vYwG4Lbpt8n5uPNFy3CanCzi6ysycKCIiHz88ccCSK9eveStt97KkeE0O3PnKlkLDlYp\nygriphX/yMhICQ4OVuGb7dvLsWPHst602VTzGBAgksc+m8XlxRUvihFiiN8kPyEEeeqXp+RS4qXC\nT9RoNDkIiwuTep/UE7/JfrI3IveuXnlx9vJZuX/2/UII0u2HbnI69nQJW1l8/jrzV2YDMHDxwKKt\n6Rk8WMTTM1OjXnnllcwspxSw2nnatKx+bUHt300p/osWLZLKlSuLi4uLTJkyRdKvbgJXrlQmf/ZZ\n/p9MESjNEDKNprxwKuaUBH4YKAEfBMixS8fyLWez2eS73d+J7yRf8ZzgKZ9v/7zM9Pbz4uqU0IXq\nxaZNSqd+/FFEiheePmKEOjUkJH977CX+Dg31zCAmJoYBAwbw6KOPUqNGDf755x/eeustzGZzViGb\nDUaOhLp14fnnr+t6ix5fhJeLV+Zzdyf3Egsh02jKC7X9arPmqTVYxUq32d04c/lMrjLh8eH0mteL\nZ5c+S/Oqzdn34j5ebPMihmE4wOKiEfpaKPfWvTfzeaF6cdddUKsWzJ5d7GtNnAjPPgshITCzZKJo\nM3GY+O/Zs4fRo0ezePFibr/9dubPn8+YMWP4+++/uf3223OfMH8+7N0L48aBi8s1XTPdls7YjWPp\nObcnIpIZQpZqTS3REDKNprzQsFJDVg1YRWxKLPfOvpd9Efvo9H0nwuPDmbt/Lk2+aMLqk6v5+P6P\nWf/0eupWqOtokwulmnc16lWoh4FqoJLTk/Fy8cpfL0wmGDAAVq+G8OKFixoGzJql4v9fegkWL75e\n6wu4lhpFlD6GYYjZbMZqtVK7dm0WLVpEy5Yt8y6clgaNGoG3N/zzj/pwi8nxqOMM/GUg285vo3/T\n/lxOvUxNn5oMaTWEWbtmEZ4QzuInSvCT1mjKEX+e+ZP7Zt+Hh7MH0cnR1ParzanYU7Sr3o7ve31P\ng0oNHG1isQieH0w1r2pU9qjMmE1jqOlbk9DXQvMfsRw5ojTro4/Y1KoVwcHBJCUlkZycTIbujRo1\nigkTJuR5elIS3HMP7N6t1gN06pT1nmEYu0Sk9XXflD18R9dykM339eSTT+bv4BJRPn5QPv9ikhFN\n4DHBQypMriDz9s8rdh0ajaZ43Mzzau+tf08IQd5b/17BBVu3FmnRQkRyZjgdMWKEdO7cWZycnGT1\n6tX5nn7pkkoE5+Mjsidbpnhu9AlfCpn4yCQhQUX3dOxY7P3QIuIj5OE5D2dGEzg6dlijKS+ExYVJ\nv4X9xHWca6boF3fj9LKKzWaTZ5c8K4QgX+36Kv+Cn3yiJPZA7t38YmNjpWnTpuLt7S179uS/B8iZ\nMyLVq4tUrSqSkTDUXuJfJP+JYRjdDcM4ahjGv4ZhjMinTGfDMPYYhnHQMIyN1z0kyeCTTyAyEiZP\nVg6xIrL86HKaftGUP078wbT7p7FqwCoCfQLtZpZGo8mfat7V8HH1wWKz4ObkRpo17aaZVzMMgy8f\n+pL7693P0BVDWXl8Zd4F+/YFsxl+/DHXW76+vqxcuRJfX18efPBBzpzJPTkOUKMG/P47xMZChw5w\n8aIdb6Sw1gEwAyeAuoALsBdofFUZP+AQUPPK8ypFqLfw3X0uXVJjnl698m9dryI+NV4GLxsshCBB\nM4P0HroajYNwRGqG0iQuJU5aftlSPCZ4yI7zO/Iu9OCDIjVqiOSzm+D+/fvF19dXGjduLNHR0fle\na/p0NYhQG8KUktsHaA+syvZ8JDDyqjLDgPHFubDZbC58d5833xQxjDyHTXmx9exWuXX6rWKEGPL2\n6revaXcfjUajKSrh8eFSe1ptqfJBFTkRfSJ3gYylu+vX51vHunXrxNnZWTp16iQpKflr1vjxqiqo\nESalJP59gK+zPR8IfHpVmWnAZ8AGYBfwVGH1FpTeQUREzp4VcXUVefrpgsuJSFp6mryz7h0xjTFJ\nrY9rycZQ++0TqtFoNAVx+OJhqTC5gtSfUT93hoDERBFvb5Hnniuwjp9++ilzQ/iC9hzv21cEWomU\nls+/CDgBrYAewP3AO4Zh1L+6kGEYQwzD2GkYxs6LhTmvxoxRjdyYMXm+HR4fTqfvO/HXmb+469u7\nGLdpHAOaDWDv0L10rNXxum9Io9FoikLDSg1Z9uQyTseepue8niRbkrPe9PCARx+FhQshOTnfOvr1\n68eUKVOYN28eI0bkOa3K+vWwZg3AheJvNpAXhbUOFM3tMwIYk+35N8BjBdVbYM//8GERk0nk9dfz\nLTJ0+VAxQgwxjzFLxSkVZcHBBfnXp9FoNCXMwoMLxQgxpPe83pJuzZaaZu1a5WSZP7/A8202m7z8\n8ssCyPTp03O8l30rSOzk8y90kZdhGE7AMeAe4DywA+gnIgezlWkEfIrq9bsA24G+InIgv3pbt24t\n+W7M0qePWtlw8iRUrpzjLfcJ7qSkp+Q6xc3JjeTR+besGo1GU9J88vcnvL7qdV654xU+6f6JWgRm\ns6l0D0FBsHx5gedbrVb69OnD0qVLWbhwIcHBwQC8/z60aaN2BLPXIq9C3T4ikg68DKwCDgM/i8hB\nwzCGGoYx9EqZw8DvwD6U8H9dkPAXyI4dsGgRvPlmLuFPt6Uz4q4RmI2snD8lubWbRqPRFIfX2r3G\nG+3eYMb2GXy49UP1oskE/fqpmM1C3N1ms5k5c+bQrl07+vfvz19//QXk3grSHjgsvUO+Pf9u3WDf\nPjhxQqVzuMLu8N0MXj6YXeG7qOlTk7NxZ3F1ciXNmsYLrV7g8x6fl6L1Go1Gkzc2sfHkoif5+eDP\nzH10Ln2b9IUDB6BpU3j4YZWgsn37Auu4dOkSd955J1FRUWzZsoUGDbLSYZRaz79UWbMG1q6F//u/\nTOFPTEtk+B/DafNVG87FnePnPj/T6pZWvNj6Rf4e9DdDWw0lIiHCwYZrNBqNwmSY+N8j/6NjrY48\nveRpNoRugPh4tUh1+XKVtGfr1gLrqFSpEr///jtOTk50796diAj7a1zZ6fnbbHDHHXDpEhw9Cq6u\nrPp3FS/++iKnYk8xuOVgpnSbQgX3Cg6xV6PRaIpDTHIMd317F2HxYfxlfYbb35muIhjNZpWdeOTI\nQuvYsWMHnTt3plGjRmzYsAEvL6+bsOe/aBHs2gVjx3Ih/TIDFg+g+0/dcTG7sPGZjcx6eJYWfo1G\nc8NQwb0Cv/X/DQ9nDx5wmsc/NZzp9AxEeBvQuXOR6mjTpg0///wzu3fvJjg4mLfffhsgyB72lY2e\nv8UCt9+OuLrwv/+9wX/XDCc+NZ5RHUYx8u6RuDq5OsRGjUajuV72ROyhw3cdcLGZiLXE8cKpinz+\nv6hi1fHmm2/y4YcfZqaDFpHr3v2mbPT8v/+ef6OO0+1ZM88uH0SjSo3YM3QPIZ1DtPBrNJobmvbf\ntCchLYHo9DhsBnxRNxpjjIH7BPci1xEZGQmoUFB74XDxt8RfZtKS/9L0JRM700KZ2WMmm57dROPK\njR1tmkaj0Vw3J189Sb8m/XAxqx0IDYGHXZs5PDzdYeJ/NOooK4+tpNXHDRl1RzwPVe3I4ZcO80Lr\nFzAZDm+TNBqNxi5kpLdOt6XjYnZBgFXJ+9kTscehdjlMZRNSE+gxtwfRcZEsPdaKBcPWc4v3LY4y\nR6PRaEqMyMRIhrYayvbntzPA2hi3NKHHnB588NcHFGXedfDgwfj7++PuXnRXUWE4bg/fWwzhBfW3\nm8mF5HdSHWKHRqPRlCrbtpHYoR3PTrqDBQnb6de0H18//DXuzgULe1JSEhMmTGDixIlWEXG6XjMc\n6l/xsED/uNqc+s9pR5qh0Wg0pUebNnhWvoX5WwKZ2HUic/fP5e7v7ubM5bx388rAw8MjY8N3u/iL\nHCb+BpBiBp/Wd90UW7tpNBpNkTCZoHdvjN9+Z2Sr11j+5HL+jf6XNl+1YfPpzaVnRqld6SoaXYSh\n/xhExJ5zlAkajUbjGIKDVX7/P/6gR/0ebHt+G35ufnT9oSszd84sFRMcJv7u6fDZbyYWx97vKBM0\nGo3GMXTsCBUrwuLFgNoQZtvz27iv3n28+OuLDF0xlDRrWoma4NiYSheXIi9z1mg0mpsGJyfo2VMl\nerNYAPBz82NZ32WMvHskX+76knt+uIfIhMgSM8Fx4h8YqDJ4FpLaVKPRaG5KeveG2FjYsCHzJbPJ\nzMR7JjLv0XnsCttF669aszMsn02vrhPHiX/Vqlr4NRpN+eXee8HTM9P1k50nmjzBlkFbMBtmOnzX\ngZ/2/WT3y+ultBqNRuMI3N3hwQdhyRKV0v4qgqoGsWPwDtoGtmXALwMY/sdwzl0+B5VokEdtxUaL\nv0aj0TiK3r0hIgL+/jvPtyt7Vmb1wNW81OYlpm6dSvtv24MzXva4tBZ/jUajcRQ9eqjAlzxcPxk4\nm535Zvc3AJyLs19ovBZ/jUajcRQ+Pmrf8sWL1S5f+ZCRGdTN7Ga3S2vx12g0GkfSuzecOgX79uVb\nJCMzaJotDcAuCdm0+Gs0Go0j6dlTpXwowPUDWZlBucRhe1y2bGzjqNFoNOWZzp0hKgr27y+06M23\ngbtGo9GUV3r3hgMH4PjxUrukFn+NRqNxNL17q8dffim1S2rx12g0GkdTsya0alWo39+eaPHXaDSa\nskBwMGzbBufPl8rltPhrNBpNWSA4WD0uWVIql9Pir9FoNGWBhg3VUUp+fy3+Go1GU1YIDlYpnqOi\nSvxSWvw1Go2mrBAcDFar2uSlhNHir9FoNGWFli1V5E8puH60+Gs0Gk1ZwTBUzP+qVZCQUKKX0uKv\n0Wg0ZYnevSE1FX77rUQvUyTxNwyju2EYRw3D+NcwjBEFlGtjGEa6YRh97GeiRqPRlCPuvhsqVy5x\n10+h4m8Yhhn4DHgAaAw8aRhG43zKTQH+sLeRGo1GU24wm6FXL1ixQo0ASginIpS5A/hXRE4CGIYx\nD+gFHLqq3CvAIqDNtRpjsVg4d+4cKSkp11rFTY+bmxvVq1fH2dnZ0aZoNJqSondv+PprWLtW7fNb\nAhRF/AOBs9menwPaZi9gGEYg0BvownWI/7lz5/D29qZ27doYhnGt1dy0iAhRUVGcO3eOOnXqONoc\njSTSv+gAABOPSURBVEZTUtxzD3h7K9dPCYm/vSZ8pwFvi0juLeizYRjGEMMwdhqGsfPixYu53k9J\nScHf318Lfz4YhoG/v78eGWk0NzuurvDQQyrVg9VaIpcoivifB2pke179ymvZaQ3MMwwjFOgDfG4Y\nxiNXVyQis0SktYi0rly5cp4X08JfMPrz0WjKCb17w6VL8OefJVJ9UcR/B3CbYRh1DMNwAfoCy7IX\nEJE6IlJbRGoDC4FhIlI62Yk0Go3mZuSBB9QIoISifgoVfxFJB14GVgGHgZ9F5KBhGEMNwxhaIlZp\nNBpNecfLC+67T+X4L4Htdovk8xeRlSJSX0TqiciEK6/NFJGZeZR9RkQW2tvQ0sJsNhMUFESTJk14\n7LHHSEpKAsDLyyvfc0JDQ5kzZ05pmajRaMoLwcFw9izs2mX3qm/8Fb5bt8KkSerRDri7u7Nnzx4O\nHDiAi4sLM2fmat9yocVfo9GUCA8/rOL+S8D1U5RQT8fw+uuwZ0/BZS5fhn37wGYDkwmaNQNf3/zL\nBwXBtGlFNqFDhw7s27ev0HIjRozg8OHDBAUF8fTTT/Of//ynyNfQaDSafPH3h06dlOtnwgS7Vn1j\n9/wvX1bCD+rx8mW7VZ2ens5vv/1G06ZNCy07efJkOnTowJ49e7TwazQa+xIcDEeOwOHDdq227Pb8\ni9JD37pVLYZISwMXF/jpJ2jf/roum5ycTFBQEKB6/oMGDbqu+jQajea6eOQRePll5fpp1Mhu1ZZd\n8S8K7dur5c8bNkDnztct/JDl89doNJoyQWAgtG2rXD+jRtmt2htb/EEJvh1E/3rw9vYmPj7eoTZo\nNJqbmOBgePttOH3ablXe2D7/MkKzZs0wm800b96cjz/+2NHmaDSam43evdXjEvutnb3xe/52JiGf\n3XPyex3A2dmZdevWlZRJGo2mvHPbbdCkiXL92And89doNJobgeBgu+b50T3/YrB//34GDhyY4zVX\nV1e2bdvmIIs0Gk25oXdvGDuWQKhqj+q0+BeDpk2b6kggjUbjGJKSwDCoKhJoj+q020ej0WhuBDZu\nBDumdNfir9FoNDcCnTuDqysCdknxqcVfo9FobgSuLGqNhDB7VKfF/yryS+lcVJYsWcKhQ1fvba/R\naDR2oH17zkOEPaq6ocU/MTGRUaNGUaFCBUaPHl1soc6La0npnB0t/hqN5kbghhX/TZs2UatWLT75\n5BNiY2P5+OOPqVmzJps2bbLbNTp06MC///4LwA8//ECzZs1o3rx5rnDPDLZs2cKyZcsYPnw4QUFB\nnDhxwm62aDQajT0ps6Ger7/+eoFhlYcPHyYqKirzeXJyMsnJyTz22GM0yifzXVBQENOKmM8/I6Vz\n9+7dOXjwIOPHj2fLli1UqlSJ6OjoPM+588476dmzJw899BB9+vQp0nU0Go3GEdywPf+SIiOlc+vW\nralZsyaDBg1i3bp1PPbYY1SqVAmAihUrOthKjUajuT7KbM+/sB76wIED+fHHH3O9ft999zF79uxr\nvq5O6azRaMoDN2zPf/Dgwfj7++Pu7g4o0fb392fw4MF2v1bXrl1ZsGBBppspP7cP6PTOGo3mxuCG\nFf+OHTty5swZ/vOf/+Dn58cb/9/e/cdWVeZ5HH9/aavt2AkDNEGhVGQgqGClTAGZiFD2x1SdwLJj\n/UWQWZlUzLARiVkhDdiZEcMIu3F2VRCVUJEMDqyzGIM77mxgO0QQ0FSmLDpFtvJjsUhREVgrnT77\nxznU28u97W3vub+8n1dywznnec453z59+Pbc55z73EWLOHLkCLfcckvg5xozZgw1NTVMnTqVG2+8\nkUWLFkWte/fdd7Ny5UrKysp0w1dE0pY5F8iHxXqtvLzc7du3r8u2gwcPRr1ZK19TO4lkLzN7xzlX\nHu9xMvbKX0RE+i5tb/imu+XLl7N58+Yu26qqqqipqUlRRCIisVPy76OamholehHJWBr2ERHJQkr+\nIiJZSMlfRCQLKfmnqSeeeCLVIYjIN5iSfwo55+jo6IhYpuQvIomU0cm/ZWMLu4bvYke/HewavouW\njS1xH7O5uZmxY8d2rq9atYra2lqmTZvGQw891PlFL3v27AGgtraWOXPmMHnyZEaNGsXzzz/fue/K\nlSuZMGECpaWlPPbYY53HHz16NPfddx9jx47l6NGjl8SwePHizgnmZs+eHffPJCISLmMf9WzZ2MIH\n1R/Qcd67cm77qI0Pqj8AYPDswQk55/nz52loaKC+vp7777+fxsZGAPbv38/u3bs5d+4cZWVl3H77\n7TQ2NtLU1MSePXtwzjFjxgzq6+spKSmhqamJuro6brrppojnWbFiBU8//bQmmBORhEnb5N+0sImz\nDWejlp/ZfQbX1nVqio7zHbw/733+9/nIX3FZOK6QUU+N6nNM99xzD+DNK3TmzBk+++wzAGbOnElB\nQQEFBQVUVFSwZ88edu7cyZtvvklZWRkAZ8+epampiZKSEq6++uqoiV9EJBliSv5mVgn8CsgBXnDO\nrQgrnw08ChjwBfCgc+69gGPtIjzx97Q9Vrm5uV3G4b/88svOZTPrUvfieqTtzjmWLFnCAw880KWs\nubmZK664Iq4YRUTi1WPyN7Mc4Bngr4BjwF4ze805F/pFtf8DTHXOfWpmtwJrgUnxBNbTFfqu4bto\n+6jtku2XX305ZTvK+nzewYMHc/LkSVpbWyksLOT111+nsrISgFdeeYWKigp27txJ//796d+/PwBb\nt25lyZIlnDt3jh07drBixQoKCgpYunQps2fPprCwkOPHj5OXlxdzHHl5eVy4cKFX+4iIxCqWK/+J\nwCHn3GEAM9sEzAQ6k79z7q2Q+ruB4iCDjGTE8hFdxvwB+n2rHyOWj4jruHl5eSxbtoyJEycydOhQ\nrr322s6y/Px8ysrKuHDhAuvWrevcXlpaSkVFBadOnWLp0qUMGTKEIUOGcPDgQSZPngxAYWEhL7/8\nMjk5OTHFUV1dTWlpKePHj2fjxo1x/UwiIuF6nNLZzO4AKp1zP/HX5wCTnHMLotR/BLj2Yv1ogpjS\nuWVjC4drDtN2pI3LSy5nxPIRCbvZO23aNFatWkV5edeZVGtrayksLOSRRx5JyHkj0ZTOItkrqCmd\nA73ha2YVwDzg5ijl1UA1QElJSdznGzx7cMKSvYjIN1ksyf84MCxkvdjf1oWZlQIvALc651ojHcg5\ntxbvfgDl5eWp+RaZPtqxY0fE7bW1tXEdd9KkSbS1db13sWHDBm644Ya4jisi0p1Ykv9eYJSZXYOX\n9O8G7g2tYGYlwKvAHOfcnwKP8hvs7bffTnUIIpKFekz+zrl2M1sA/A7vUc91zrkDZjbfL18DLAMG\nAc/6jz22BzEmJSIiiRHTmL9zbhuwLWzbmpDlnwDd3uAVEZH0kdFz+4iISN8o+YuIZCElfxGRLJSx\nyf/JJ2H79q7btm/3tscjJyenc9rmqqoqzp8/36v96+vrGT9+PLm5uWzZsiW+YEREEiRjk/+ECXDn\nnV//Adi+3VufMCG+4xYUFNDQ0EBjYyOXXXYZa9as6XmnECUlJaxfv557772358oiIimStlM6L1wI\nPU1nP2QI/OAHcNVVcOIEXHcd/Oxn3iuScePgqadij2HKlCns378fgJdeeolVq1ZhZpSWlrJhw4aI\n+wwfPhyAfv0y9u+qiGSBtE3+sRgwwEv8R45ASYm3HpT29nbeeOMNKisrOXDgAI8//jhvvfUWRUVF\nnD59OrgTiYikQNom/1iu0C8O9SxdCqtXw2OPQUVFfOe9+PWJ4F35z5s3j+eee46qqiqKiooAGDhw\nYHwnERFJsbRN/j25mPh/8xsv4VdUdF3vq4tj/iIi32QZOzC9d2/XRF9R4a3v3Rv8uaZPn87mzZtp\nbfXmq9Owj4hkuh7n80+UIObzT4TCwkLOnr30u4Pr6upYuXIlOTk5lJWVsX79+oj77927l1mzZvHp\np5+Sn5/PlVdeyYEDBwKNMR3aSURSIy3n8/8miJT4AebOncvcuXN73H/ChAkcO3Ys6LBERAKVscM+\nIiLSd7ry76Ply5ezefPmLtuqqqqoqalJUUQiIrFT8u+jmpoaJXoRyVga9hERyUJK/iIiWUjJX0Qk\nCyn5i4hkoYxP/ie+OMHU9VP5+OzHgRwv3vn829rauOuuuxg5ciSTJk2iubk5kLhERIKU8cn/F/W/\nYOeRnfz8v34eyPHinc//xRdfZMCAARw6dIiHH36YRx99NJC4RESClLaPei7894U0fBx9grU/HPkD\nHa6jc331vtWs3reaftaPKSVTIu4z7spxPFUZ+4T+fZnPf+vWrdTW1gJwxx13sGDBApxzmFnM5xUR\nSbS0Tf49mThkIoc/Pcyp/ztFh+ugn/Wj6FtFfHfAdwM5fl/n8z9+/DjDhg0DIDc3l/79+9Pa2to5\nHbSISDpI2+QfyxX6g68/yNp315Kfm89Xf/6KH133I569/dm4zqv5/EUkG6Rt8o9Fy7kW5n9vPtXf\nq2btO2s5cfZE3MeMdz7/oUOHcvToUYqLi2lvb+fzzz9n0KBBccclIhKkjE7+r971aufyM7c/k7Dz\nTJ8+nVmzZrFo0SIGDRrE6dOno179z5gxg7q6OiZPnsyWLVuYPn26xvtFJO1kdPJPljFjxlBTU8PU\nqVN7nM9/3rx5zJkzh5EjRzJw4EA2bdqU3GBFRGKg5B8m3vn88/PzL5ntU0Qk3WT8c/4iItJ7uvLv\nI83nLyKZTMm/jzSfv4hksrQb9knVF8pnCrWPiAQhrZJ/fn4+ra2tSnBROOdobW0lPz8/1aGISIZL\nq2Gf4uJijh07xieffJLqUNJWfn4+xcXFqQ5DRDJcTMnfzCqBXwE5wAvOuRVh5eaX3wacB37snHu3\nt8Hk5eVxzTXX9HY3ERHppR6HfcwsB3gGuBW4HrjHzK4Pq3YrMMp/VQOrA45TREQCFMuY/0TgkHPu\nsHPuK2ATMDOszkzgJefZDXzHzK4KOFYREQlILMl/KHA0ZP2Yv623dUREJE0k9YavmVXjDQsBtJlZ\nYzLP30dFwKlUBxEDxRmsTIgzE2IExRm00UEcJJbkfxwYFrJe7G/rbR2cc2uBtQBmts85V96raFNA\ncQZLcQYnE2IExRk0M9sXxHFiGfbZC4wys2vM7DLgbuC1sDqvAfeZ5ybgc+dc/JPri4hIQvR45e+c\nazezBcDv8B71XOecO2Bm8/3yNcA2vMc8D+E96vl3iQtZRETiFdOYv3NuG16CD922JmTZAT/t5bnX\n9rJ+qijOYCnO4GRCjKA4gxZInKapFEREsk9aze0jIiLJkfDkb2aVZvaBmR0ys8URys3M/tkv329m\n4xMdU4QYhpnZdjP7bzM7YGYPRagzzcw+N7MG/7Us2XH6cTSb2R/9GC65658m7Tk6pJ0azOyMmS0M\nq5OS9jSzdWZ2MvQxYzMbaGb/YWZN/r8DouzbbV9OcIwrzex9/3f6WzP7TpR9u+0fSYiz1syOh/xe\nb4uyb1Lasps4XwmJsdnMGqLsm8z2jJiHEtY/nXMJe+HdIP4QGAFcBrwHXB9W5zbgDcCAm4C3ExlT\nlDivAsb7y98G/hQhzmnA68mOLUKszUBRN+Upb88IfeBj4Op0aE/gFmA80Biy7Ulgsb+8GPhllJ+j\n276c4Bj/Gsj1l38ZKcZY+kcS4qwFHomhTySlLaPFGVb+j8CyNGjPiHkoUf0z0Vf+GTE1hHPuhPMn\nonPOfQEcJHM/oZzy9gzzF8CHzrmPUhhDJ+dcPXA6bPNMoM5frgP+JsKusfTlhMXonHvTOdfur+7G\n+yxNSkVpy1gkrS2h+zjNzIA7gV8n6vyx6iYPJaR/Jjr5Z9zUEGY2HCgD3o5Q/H3/bfcbZjYmqYF9\nzQG/N7N3zPvEdLi0ak+8z4VE+4+VDu0JMNh9/bmUj4HBEeqkU7vej/fuLpKe+kcy/L3/e10XZYgi\nndpyCtDinGuKUp6S9gzLQwnpn7rhG8LMCoF/BRY6586EFb8LlDjnSoF/Af4t2fH5bnbOjcObSfWn\nZnZLiuLokXkfCpwBbI5QnC7t2YXz3kOn7SNwZlYDtAMbo1RJdf9YjTf0MA44gTekks7uofur/qS3\nZ3d5KMj+mejkH9jUEIlmZnl4Db7ROfdqeLlz7oxz7qy/vA3IM7OiJIeJc+64/+9J4Ld4b/dCpUV7\n+m4F3nXOtYQXpEt7+louDo35/56MUCfl7WpmPwZ+CMz2k8AlYugfCeWca3HO/dk51wE8H+X8KW9L\nADPLBf4WeCVanWS3Z5Q8lJD+mejknxFTQ/jjfi8CB51z/xSlzpV+PcxsIl7btSYvSjCzK8zs2xeX\n8W4Chk+Ol/L2DBH1qiod2jPEa8Bcf3kusDVCnVj6csKY94VK/wDMcM6dj1Inlv6RUGH3l2ZFOX9K\n2zLEXwLvO+eORSpMdnt2k4cS0z+TcAf7Nry71h8CNf62+cB8f9nwvizmQ+CPQHmiY4oQ4814b6X2\nAw3+67awOBcAB/Duou8Gvp+COEf453/PjyUt29OP4wq8ZN4/ZFvK2xPvj9EJ4ALeuOg8YBDwn0AT\n8HtgoF93CLCtu76cxBgP4Y3pXuyfa8JjjNY/khznBr/f7cdLPlelsi2jxelvX3+xP4bUTWV7RstD\nCemf+oSviEgW0g1fEZEspOQvIpKFlPxFRLKQkr+ISBZS8hcRyUJK/iIiWUjJX0QkCyn5i4hkof8H\nwKbkuCzDki8AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197f20f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18700)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "[1 1 1 0 1 0 1 1 0 1 1 0 0 0 0 0 0 0 0 0 0]\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz\nAAALEgAACxIB0t1+/AAAIABJREFUeJzsnXd4FFX3x7+zm2w2vdMJTapIF6V3pSgdqRZepFpeRBGE\nV38JHUSaSrWCKB2lIyShKKCAgICRHjohEBJIT3a+vz9usmm7yW6y6ffzPPNkd+bes2cmM+feOffc\ncxWSkEgkEknpQlPYCkgkEomk4JHGXyKRSEoh0vhLJBJJKUQaf4lEIimFSOMvkUgkpRBp/CUSiaQU\nIo2/RCKRlEKk8ZdIJJJSSI7GX1GUbxRFua8oyjkzxxVFUZYoinJZUZS/FUVpYns1JRKJRGJL7Cwo\n8x2ALwCsNnO8G4CaKdtzAJal/M0WHx8fVq1a1SIlJRKJRCI4efLkA5K+eZWTo/EneUhRlKrZFOkF\nYDVFnohjiqJ4KIpSnuTd7ORWrVoVJ06csEpZiUQiKe0oinLdFnJs4fOvCOBmuu+3UvZJJBKJpIhS\noAO+iqKMUhTlhKIoJ8LDwwvypyUSiUSSDlsY/9sAKqf7XillXxZIriTZjGQzX988u6wkEolEkkts\nYfy3AXgtJerneQBROfn7JRKJRFK45DjgqyjKTwDaA/BRFOUWgP8DYA8AJJcD2AWgO4DLAGIBDM8v\nZSUSiURiGyyJ9hmcw3ECeMtmGkkkEokk35EzfCUSiaQUIo2/RCKRlEKk8ZdIJJJSiDT+EolEUgqR\nxl8ikUhKIdL4SyQSSSlEGn+JRCIphUjjL5FIJKUQafwlEomkFCKNv0QikZRCpPGXSCSSUog0/hKJ\nRFIKkcZfIpFISiHS+EskEkkpRBp/iUQiKYVI4y+RSCSlEGn8JRKJpBQijb9EIpGUQqTxl0gkklKI\nNP4SiURSCpHGXyKxNUePArNni78SSRHFrrAVKNYcPQoEBQEdOwItWhS2NpKiwNGjQKdOQGIioNMB\ngYHy3pAUSaTxzy1HjwLt2gFJSYBeLxoB+ZBLgoKAuDjxOTEROHBA3heSIol0++SWRYuE4QeAhATx\nkEtKNyRw/HjGfe3bF4oqEklOSOOfG/buBTZvBjQaQFHEQ1+jRmFrJSls/P2BX34Bhg0Tb4UGA+Dt\nXdhaSSQmkcbfWv78E+jXD3jmGWDPHmDyZMDeHvj118LWTFKYLF0KTJsGDB8OrF4NbNgAODoCc+YU\ntmYSiUmk8beGCxeAHj2AMmWA3buBLl2AWbOAUaPEA3/zZmFrWLwprlEyGzYAb78NvPwysHKleBss\nUwYYORJYswa4fr2wNZRIsiCNv6XcuQO8+KJ4sH/9FShXLu3Yhx8K18+nnxaefsWd1AH0qVNFtExx\naQACA4Wbp2VLYN06wC5dDMXEieJ+mTev8PSTSMwgjb8lREYCXbsCDx+KHv9TT2U87ucHvPoqsGoV\nEBZWODoWdxYvFgPoZFqUTFHn5Emgd2+gdm1g+3bAySnj8UqVgDfeAL7+Grh7t1BUlEjMIY1/TsTF\nAT17Av/+C2zdCjRtarrc5MnCaC1cWLD6lQROnxbXVpPudizqUTKXLgHduokB3T17AE9P0+UmTRKN\n2mefFax+EkkOSOOfHQYDMGQI8NtvwnfbubP5srVqAQMGiIG/R48KTsfizsOHQJ8+gK8vsG1bWkx8\ntWqFq1d23L0LvPCCeEvZuxeoWNF82Ro1xD20fDnw4EHB6SiR5IA0/uYggXHjgJ9/Fi6JgQNzrjNl\nCvDkCfDFF/mvX0kgOVlc17t3gS1bxGD699+LRnfFisLWzjSpLsDwcGDXLuHyyYmPPgJiYsR9JJEU\nFUgWyta0aVMWaT75hATIKVOsq/fyy6SXF/nkSf7oZYojR8hZs8Tf4sT774tr/M03Gfd3706WLUvG\nxxeOXuaIjSXbtiXt7clff7Wubr9+pLs7GRmZP7pJSg0ATtAGNlgaf1N8+aW4NP/5D6mq1tU9elTU\nnT8/f3TLzJEjwhhpNKSjY/FpANauFdfprbeyHtu7Vxxbs6bg9TJHUhLZuzepKORPP1lf/6+/xDnN\nmmV73SSligI1/gC6ArgA4DKAySaOuwPYDuAMgPMAhucks8ga/40bxQP+8svigc8NHTuS5cqRcXG2\n1c0UQ4eKfyNAarXFw7icOiUaqjZtyMTErMdVlaxTh2zWzPrGNz9QVfLNN8U1XrIk93K6dSN9fMjo\naNvpJil1FJjxB6AFcAVAdQC6FANfL1OZKQDmpnz2BRABQJed3CJp/IOCSJ2ObNWKjInJvZzAQHFp\nly61nW6muHWLdHVNM/4ODkW/5x8eTlapQlaqRN67Z75c6ttXUTifKVOELlOn5k3O778LOQsX2kYv\nSanEVsbfkgHf5gAuk7xKMhHAOgC9Mg8dAHBVFEUB4JJi/JOtHX8oVE6dAnr1AmrWFFEnmWO2raFD\nB+D554G5c9OSv9kaVQVef10Mjn7/vcgs2rZtoWSQDFsbhqNVj+KA5gCOVj2KsLVm5jqkDvDeuycG\neMuWNS9D3wNwdweWLLG9HtbIeG21mMU9ciQwfXredGjZUoSwfvqpSAZYkOdRCDKKgg5FRYYtdaiF\nWmbiza1DEQ1JNgUUpT+AriTfTPn+KoDnSL6drowrgG0A6gBwBTCQ5M7s5DZr1ownTpzIo/o24upV\n8WDqdMCRI2JyTl7ZsUNM9//uO2Gkbc2nnwIffoiwET/h6v4qSLgeDwfcR/UVzVB2lHVJ5sLWhuHq\n1KtIuJEABz8HVJ9ZHWWHls25YkrdC6MuQI1Vjfs0ThrUXlk7q4z33wcWLAC+/VZMfspJRrvjKLtv\nMhAamn04pbV6mIAkwtaG4eLoixllIB41mxxE2cPTwNR5CEzZUj6nPkP319/H5XcuQ41LV99RgxqL\na6DMgDKizsFDQJ/ewGcL0q5Bukfw/sb7uDLhSlYZC2rAt59vhrIZTyDtY/jmcFz5IKuM6vOrw7ev\nb47XAgDCt4Tj6gdXs8r41DIZ4VvCcXWiifrzrNThw+Ivw9Y6jMZoXOAFxaKK2WAr498fQCsAEwDU\nALAPQEOSjzPJGgVgFAD4+fk1vV4Ucp6EhQGtWonY/N9+A+rWtY1cEmjcGIiPB86fB7Ra28gFgL/+\nAp5/HmENJ+DCP90zGit7FbW/fdpi431v7T1cHHkx442p18Bvqh88O3pCTVChJqhgAo2f038PnR4K\nQ6Qhi1yNiwZlXikDGggmE7xyHTz2J1itJlivvtiXsj0+9hhMyHofKnaAY/J10MsH8PIBVQIqsv41\nEEkPkwA1iwhAEedjNNIq0j6nGvHsHwGJpEhhK+NvyWIutwFUTve9Usq+9AwHMCfFH3VZUZRrEG8B\nf6YvRHIlgJWA6PnnVmmbsX+/6JU/eAAcPGg7ww+InC5Tpgg3x9atQP/+WYrkqscdEwMMGQL6lsHV\nuy9Djc3oVlKTNLj0ziXEXYmD4YkBhmgDkp8ki88p3w1PUvZFG2CIymq41XgVoR+HIvTj0Fyfvhqt\nImJvBBQ7BYohEcrtB1Cc6kBxrQ7lXqLYr1Wg2CkmDT8AMBlwrpAA5cEpoEsvKPZ2gAZQNEqWv3eW\n3zGtCIGKb1UEFIiyiiI+p2zG7xrgeoD5zki1mdXS6iFdPaTJujrxqtn6NRbWSKt37hzw1VfA0CFA\n8+ZpMgBcfveyWRk1v6iZoWwWUvZfGnfJvIxlNc0eS8+lseZl1FpeK8f6F8dcNF9/Rc71AeDi6JIh\nI791yC2W9PztAFwE0AnC6B8HMITk+XRllgEII+mvKEpZAH9B9PzNTmksdLfP4cPC/6qqwt2THysu\nGQzA00+L8YOTJ0WDkII5N0WN+TXg3todSfeTkBiWiMT7iUgKS0Li/UQkhiUi6c9LSAxPRpKuDNTE\n7Bt/jaMGWhcttK4pm4sWdq52xs9aVy1uL8ncjqegAA12N4DioEDjoDFumb8ff+Y4Em5m9V87VHFA\ni9AWYjJUs2biOp88KbJdZuJo1aNIuG5GxjdxItHbN9+IdMlmyFZGqGX/17zKsLh+6lthQoJ4K0yX\n1qIonIctZBQFHYqKDFvrYKuef44DviSTAbwNYC+AEAAbSJ5XFGWMoihjUopNB9BSUZSzAAIBTMrO\n8BcJZs8WBgkQRjo/EolptSLnz6lTwO7dIInE+4mIPByJS+9eymD4AUCNVXFp3CWcaHACZzqfQcjQ\nEFx57wpuzr+JiD0RSPznPuzCr8Dj6SRUfNcPdh6mX9wcnGPQNqkt2sa2Rav7rfD8lefx7Oln0eS3\nJmiwuwGe3vA06nxTBzUX14RDFQfTMvwc4PWiFzzbe8K9hTtcm7jC+WlnOD3lBH1lPXRldLBzt0P1\n2dWhccp4G2mcNKg+s3raAG9YmHj7MWH4AaD6zGxkdOgA1K8vBn6z6ahkK8NCqs+sBo2S8SHV2Bss\nlrGrRX2cdsiY4+e0gyd2taifsWDqW+G//4qB7ww62OI8Cl9GUdChqMjILx3yikVr+JLcBWBXpn3L\n032+A+AFm2qWn1y5IlLxpq7EpdNZlUhs3jygRlQEKqy9YHTZ3BlaG1fcvfDhh4CaoCLuShxiL8Qi\n9lY7xDpPQ9zAB4i1/x3Jj3IOgqq3vh50ZXWwL2MPXVkd7DzsoNy9AzRoADStJgaldTq4NHLJ+vag\nTUb15GXQxLcDXFxy/K3qM6ubfAOx9MZMdVOZdF+99x4QHCyikZo1y50MAHj3XbFmwm+/AW3a5E6G\nJeeiCQb4Na5iJBLgCwclHNWnVLBYRsdRrui/8xkEeISg/v1wnCvji+mxdbFplImHtl8/kRpixgzx\nOeWt0CbnUQRkFAUdiooMW+sAGw2V5uj2yS8Kze1DigRtJ06IZG3nzwvDb4XLZ/PUCIyY5YKPEIIK\niEc4HDAN9TC7xlU0RiTir8VnGHzUuSXB6fFZOPVsAscONeBU2wkX3ryAxDuJWWSbfBVUVZFI7OhR\nMdibLp9MlnGDN5JRNqCt8CmPGGHR+eQl2scsa9YAr70mDHcOOW3mzQOefVZ08lMJDhbL4X74IYDY\nWKByZVFg06a86WWO8HCgXj2genWRgTPVLWilKzA4WOT3699frPS5YUPG88rA99+LiJ8dO0ReI4nE\nAhRFOUnSfG/KUmwxWSA3W6FN8vrqKzHRZvlyq6vGRhm4Y8ljvuEQyup4QkAVE2th4GhcYrDdAZ4b\ncI5X/3eV9364x6jjUUyKShI5YcqWJTt3Nsq698M9HnQ6yGAEG7eDTgd57wcTE58+/VTovGpVht1z\n54p5aekJClQ5t8x88vnnrT4/m3HyJKnXk+3amZ7Bm4mgIDHxNTBQTKZN/Z7h3CZNEiksrl/PH52H\nDBFpMs6etbpqRAT588/kf/9LNmiQNueuUiWRAsjsJOXERLJqVfG/KgozmSXFAsjcPrng9m2RXKtd\nO9JgyLF4fIyBv654woldIvic12M6IJkAqUBlLTxmPUQSIJ2QRICsj0ju22fmOZ43T1zuP/4w7rr3\nwz0eqXKEwUowj1Q5YtrwnzwpjFKfPlkEZzaSxu9jN4jfyoUhyzP375N+fmTlymRYWI7FY2LIbdvI\nHj2EbXd1Fdv+/ZkKXr8u0ldMmmR7nbdvF9fL39+i4lFR5I4dIi9dkyYiGwgg2rvGjUknJ/KFF8T5\nAGTLltk0AsuWiUKBgbY9J0mJRRp/a1FVkZhLr+fcieHcNOVhBsO7acpDzp6p8uCaaE7pEcHWvlF0\nTDHqAPmUPoavN4rg6ilR3FPhDy7AKbojga/iGt2RwL64QV9tPAGydeu0XqyRx49JT0+yZ0/LdY6O\nJmvXJitWJB88MHlKX30ljE3fvukagvBw0WCMH5/ny2YViYlk+/bCCp44YbbYjRvC5nXvLooCwuDX\nrZvWa27enDx2LFPF/v3FNcxL6o3MREWJ61u/PufOTMr6JhVETp8ucs1Nnkw+95xogwCRCaRdO9Fm\nHDxI7tlDenurHDJkFT08PDhw4Dd0dlbp45NNIxAXR5YvL/JBSSQWII2/tWzcKE537lxumvKQ7kjg\nfJzi1/iTvXGTdjBQn87YV9HFcvDTj7hqQiRv/ZOQQdSCYafohgQuwCkGI5gLIL7PHXyaX3xBVqgg\nZLRtm8l14e8vDvz9t2U6jxolupWZeoWXLgmDVK9emrEEyKeeEul+SJKvvCJSSxdEcjlS5OBp2VIo\nsnp1hkMGgzDkU6eSDRum6Vu9unCV7N8vjKuPD/m//4mGwMtLlHn1VfHCRpI8dEjsXLnSdnqPGSO6\n6H/8YXxz2r1bXPJhw0g7uzRjb2cnTnHqVKFzbGxGUaNHX6GbWy86OTkRAB0dHenm1otvvnmVS5cK\nN5DJRuCzz1hk8hhJijzS+FvDw4fC596kCZmUxPVlTrIj7lFJ8dkDpDfi2cPuDpeOi+TVk9nnkW/U\n6Ec2xgT+hJ8YiED+hJ/YGBPYuLFI9RsXR37+eVoj0K4dGRycooeLCzl4cM46b90qKn/4IUlh1D/7\njHz22TTj2aaNMJ5eXsLNAJDOziI9vrr3V7EjN+mHrWDuXDLo83OiG5xiIYM+P8dp08hNm8g33iDL\nlKEx6WjbtsIDFhKSZvyCgjL2mIcMWUUvL5VDhgixzs4iWWlcrCr8Kk8/bRsf+YEDQrEJE6iq5G+/\npV3H1K1OHeFp2rMn5yUaBg8enH7OsHEbNmwYSbE8wbJlwiOWoRF4Ek16ewvfl0SSA9L4W8MbbzBO\nceHKIZfY3PMxAVIDlZUQQ4Dsi5ti0FUJzlbMgwcPuHDhQrq5uZl8yAdnMupxcSIDcPny4kq3b08e\nGLRM9DQvXTL/Q7dvk97eDG/Qkcs+T2K7dml+5aZNxfjvjRtZff5r1ojeKUB2fVHljUotyE6d8nbt\nciAoiPRximYQ2vMqqvJtfE57TbKxt+zhIdq6tWtF22cKcz3m0aOv8MoVMdwBkNWqkVveDqRqCx95\nbCz51FN8UKUJF86NN75FubiI9iVdu5sjFy5c4MSJE+ng4GDRfZG5EWjRgtz7+lpxXn/9lbfzkpR4\npPG3kDOzf+NI7KUHEgiQZbXxHOFwjZ/gbAaf/QKc4pEqWV+7VVXlgQMHOGTIEOPD7ePjY/Ih9/Ly\n4tWrV7PIiI0lFy8WKf4BsoMmmGPqBJn0Lwf4G7i63mx20+yhnZ1q7H0GBJAXLmQsP3cuuXNnLD/6\n6CN6eHhwypQp3Lkzjr16iXEAN4c4foX/UL18xZaXNAMGAzmr8Trap1xfgKxcNo4ffCA61hYE+3DQ\noEHZ9phJ4WapX1/I72h/kH93eDfXOquqaISH4Ac62Ccbxxi++orcuVM0qB9/bCLiKB1xcXFcu3Yt\n27dvTwDUarWsXLmy2fviypWs/4P4eBF0ZmwEtMc4ouq+LO1aUJD4X0skpDT+2RIbZeDydx7xWfeo\nlF6+gZ3KPeL66VFMSlCNPv/0Pnt3JHDTlLSuaXh4OOfPn8/atWsTAN3d3fn222/zzJkzPHjwIL29\nveno6Gjsqbq6utLZ2ZkeHh7cunWrab1iyUWLyHJOIkrI3k7l4sViv7+/cHHYa8S4QxXvx5w0iTx9\n2ryHI1WP9D1mb29vHjx4kFeukO1bxBEgX6h+0eYRknFxwljWe0oMcrtqxVvUW/3uWCwjLCyMn3zy\nidkec3rjT4q1db78kvRyjKEGyRw3LNLUOLhZ7t8Xb021/MR1cdfF8K23xDUms4meStcAnD9/nuPH\nj6eXlxcBsFq1apw1axbv3Llj9r5wcXGhm5sbN27caFIvYyPg9kh4zrQq587NJuxVUqoptcZ/7lya\njNSZO5c8tSuWI5pG0F1JJECWV2I4Eet57aujWWT8OPEud7ntYiACucttF3+aeI9z5qgMCgrioEGD\nqNPpCIAtW7bkd999x5hMESYxMTGcMmUKPTw8OHXqVMbExPDKlSts2rQpAfC9995jQkLGgeJUYi/c\n4ELNBHo6RBt94QDp6ZbEdzSf80jbSVQNOfu0hw0blq3RNBjIpfW/oLMSTVdXlStW5N1V/uCBGGxO\n9eM3dL3Myfbz6eNlyLG3nEpISAhHjhxpNPoVK1Y0eR6NGzemakLhh2dv823lC2qVZHp6CtfarFkm\n5jwEkXPmkPv2ifFve3uhcyunv/i9+zuMuf0oQ3lTb1K7dsVx+vQEfv/992zVqhUB0N7engMGDOC+\nfftoyBQybOq+uHbtGps3b04AfPvttxlvZm3i+FvhXG7/Nn0dIo0D4t7e0vBLMlJqjX/mXvtcnKYT\nkviULsWQwsAuFSO5acw5JkNLjhuXRYapHrOTkxMrVapEAPTw8OC7777Ls7mIk4+Pj+c777xDAGze\nvDmvXbtmuuDw4Yxx8OQL7UTPeejAJCbVqmc2rDMzJ0+eNGs0M/SYt27lNVRhxwb3CYh5ZuZUyo6L\nF8mxY8Xqi4BYkXB/wGEGoj19nGOy7S2Twn0WHBzMHj16EAD1ej1HjRrFkJAQkz3m1Mb3jTfeMG0s\nBw3iWZfn2al9yptSFTGFI/V3N20Srq/U8RYvLxH5ev7d5WLHzz9nEZn5vtDr9dTr9XR2diYA1qxZ\nk/PmzWOYBfMXMpOQkMAJEyYQAJs0acLLly+bLjhhAhM0er7YRrxJlS2b/YJnktJHqTX+R6oc4QKc\noisSWReRxoidCojl5K4RvHE2gUxIEBEhlSqJOO5MmOsx+/r6cvXq1YzNHMOXCzZu3Eg3Nzd6eHjw\nl19+yVrgwgUGKR3p4/hE9Jj1jxmEDjkOZJ4+fZq9e/c29kBzHGBMTCTLlaP6ck+uWCEGNF1cxAqT\nOc1zS42ASV23XKcTa9qfO0fhq6pShXPLfsagfckZ6qX3UScmJnLt2rVs0qSJcbzE39+f9+/fz1An\nc485Ojqa//d//0cAbNOmTZbyPHqUBKh+8SV//ln0kgGhY506NI4/tG8vBpvj4ihCjHQ68RpgAnP3\nRdWqVRkcHGzyLcRafvnlF3p6etLNzY0bNmzIWuD2bQbZdaGP/jGHDBHnUKECaWIoSVJKKbXGfyt+\nYy/cYmpqhSqI5nycYiCC0wqlxtPv2GFSRmrvMycfc165fPmy0ehNmDCBielGP4OCSB+HKAY5die/\n/ZZBaE8fxydmX/H//vtv9uvXzzj+EBAQwJ07d2boMac2Bo0bN+bjx4/TKk+eLHxLt28zNJTs0iXN\nMH74YdZe+r59Isb9uefSes1Tp5J376Yr9Mkn4uCBA4yOjs7gKomJiWFUVBTnz59vHAStVasWV6xY\nYXXD+tNPP9HBwYHVqlXjuXPn0g6oqoh7rVOHNBgYHy9cPKmunRYtMg2QGwwittLLy2RXWlVVtmvX\nrkDui9DQUD733HMEwLfeeotx6eZiBAWldAQ0ncjJk/nFhCtUFDG37cwZm6ohKaaUOuMf/SiZk7tG\n0BlJVKBSh2T2x42skTrnzgkLMGRIFhl37tzhiBEjTD7g+fGQk8IN9NZbbxEAn3/+eV5PGXmdO5cM\nWnU5rYtaqxaD9iZmieo4d+4cBwwYQAB0dXXlJ598wkeP0nzVmXvMq1atolarZbNmzdJ6yxcvit+Y\nOZOksJurVonJVA4O4k1g/34Rx/7222lpCWrUIL/4Qkw0zsDly6Li4MFmXSWpDVK7du24bdu2LL5x\nazh27BjLlStHNzc37tq1K+3ADz8IRffsIZnmcpoyxYTr6fPPRdlME9BI8vjx42YNf37dFwkJCXz/\n/feNjfWllNDfuXPJIP+DQldFIR0d+c2US3RzE26tw4dtroqkmFFqjL/BoHLZ25EspxURGvWdntAV\niaYjdZKTRXfVx0eEdqQQHR3NgIAAOjs7GwfrvLy8MviYU6Nk8osNGzbQ1dWVnp6e3L59u9h55Eia\npdXrM8zwDAkJ4aBBg6goCl1cXDh16lQ+NBcon4nt27dTr9ezdu3axsaG7dsL30g6I3zjBtm1q/h5\nOzthzwHhMduyRVxOk7z8smgxbt826yqpUqUKjx8/nptLZZIbN26wUaNG1Gg0XLRokXDBJCSI+Nnu\n3bOP1AkNFTPFunbNMOJ9/fp1o/4+Pj4cP358lrGH/L4vUt1Arq6uXL9+vdg5a1Zap0CrJWfNYmgo\nWauWuE1Sbx9J6aRUGP89S5/waWcxkFvLMYY/z3+cbbQPFy4Up7R2LUnSYDDw22+/ZYUKFQiA/fr1\nM/awTEVlWMudx3fY9tu2vPvkbs6FSV66dImNGjUiAH7wwQd89PHH/AigB8ApAGP8/XnhwgUOHTqU\nGo2Gzs7OnDx5MsPDw63W7dChQ3Rzc2OlSpX4zz//pPWSM40pqCr57bdiMhZADh+eg+AdO0TBefNI\nWhajbyuio6PZp08fAuCoUaOEGy3FxTd3YrjJaJ+5c1TyxRdFYxUaSpKMioriRx99RL1eTwcHB06e\nPJmRkZEkC+e+CA0N5fPPP08AHDt2LB/s2sWPNBpxX2g0jEk5sfv3yWbNRHvw/fdWqyUpIZRo438u\nMI5dKopwNx9NPBeOeMSkhBwG265eFeEd3buTqsrAwECjoW3evDkP58P78tgdY6kJ0HDsjrEW14mL\ni+PYsWMJgHZaLR1TjKUeoIO9PRVFoZOTEydOnJh1kNNKTp06xbJly9Lb25t/Hj4sHMcmUkuk9pJz\nDNWMixO+oDp1yIQEbt++3RgJUxDGnxQN+kcffUQA7NChAx+GhAg33zvvmK7w/ffiNv/iCyYlJXHp\n0qX09fUlAA4dOpShKQ2CLcnNfZGYmMgPPviAgJgwpk+JdnLUaDK8fTx+LCZtAyLdh6T0USKNf9jV\nRA5v8oh2MNARyXyvQwSjwsz5HtKhqiKG0cWFIUFBfOmll4yuhx9//DFP/mZT6GfoCX9k2fQz9BbL\naN26tUmjWadOHd6zYWzfpUuXWLVqVbq4uHB/794i2iVdKKklE5uMTJ9OAry2Zg179eplvMZubm4F\n6iohydWrV1On07FmzZr8t2dP0bPPHNl17x7p6Um1ZUtu/+UX1qlThwDYtm1bm7qkUrHFfZE6Yzi7\nxjQ+XiQ4BUTeIbkUQOmiRBn/uCcGftI7gq5KIhWo7FfrEa+dyj65Wga+/Zb3AY5r25ZarZZubm6c\nM2dOhijxN/YjAAAgAElEQVQKW3Ln8R12/K4jFX+F8Aftptlx6OahFr/mkzlP0LIlt2/fZv369amz\nt+dmQEwzTsHkgjCm0gmEhjJer+eMp582zouYM2cOExISbOIqyQ2//fYbfX196eHqyn2AyKGRngED\n+JedHTumuFRq1qzJrVu32iRk0xQ3I2+y6YqmRqOfn/dFcrJISAqQI0aI2c+S0kGxN/61UIu/Vz7C\nd54Lp7tGzMh93usxj27M2XCkDy388K23OE2vp5tWS61Wy3HjxuXZXZIdEbER/M/P/zE+4JoADeEP\ntv66tVVyCtL4k2RERARbtGhBDcBVFSpY3V3c17o1aymKcezkej6sqGWtr5wkr127xvr161MLcKGP\nDz+aPJkeHh58u1s3DgWoKAq9vb25ZMmSDKG2tub8/fNs8VUL432hDdAS/mDLr1paJcea+0JVhasO\nEHMxCip7t6RwKfbG3w91U5ZCJMtq4rguIIoGC1IaZA4tVFIejhaNGomBzXxk0/lNLDe/HLUBWtZa\nUoujto3iidsnWHVhVcIf3HjedO4WU5ia1Zrf7pLo6Gh2ffppAuAcEzOfTXHr1i2+0rYtAfApb2/u\n3r073/TLja+cJB8/fswWNWuKcRSNJoPRHPTKKxlCY21NQnICAw4EUDddR6+5Xmy6oinH7hjLk7dP\nsvri6oQ/uPbvtRbLy3xfKCnb6u++M1tn8WLxJLdrR6aMW0tKMMXe+ANNqUBlf1znocqWL2JR0D1m\nUvRI+67vS/iDjZc35l93MqbdjU2MZauvW1E3Xcfga8EWyy0Md0nCgwccrNUaI47MuUASExM5f/58\nuri4UK8onOblxbh8siy28JUPzSGXfn5w7OYx1l9an/AHB28azLDojGkf4pLi2P679rSfZs9fL/9q\nsdz098VbXbqwDMDKZcrwlnGlnqysXSvCdRs1kukgSjolwvgPwnWL8uinYjAY2KxZswJ7yFVV5Vcn\nv6LHHA/qZ+g55/AcJhlMO1cfxj5kvS/r0W22G8/cK9ipmNa6SwzDh/MtOzsC4PDhwxkZGZlhhu7e\nvXv5dMobQo86dXgFINNPrrIhBtXAgAMBRjcJ/EGNv4YDNw4ssmMo0QnRHL97PBV/hZUWVOL2C+YD\n7yPjItlgWQO6zHLhidvml7Y0y+PH/Mvenq46HevXr8+IiAizRXftErmXvL2N0c5GZFrokkOxN/5e\neCbbPPqZuXbtGjt06GDyAc+Ph/zyw8vs+H1Hwh9s9207XnxwMcc6NyJvsNKCSiw/vzyvPbpmU32y\nw2p3yZEjVAH+38svEwB1Op3RzaBNeSsoW7Ysf/7qK6pOTmSvXvmi95WIK2z3bTvCH/Rb4EfFX6H9\nNHvCH6y6qCqTDRZEeqVQUMZ/7+W9rLpIuPnG7RjHqPisuaMyc/vxbVZZWIW+83x56WE2i/iY4+WX\nGejrS3t7e7Zp0ybbFBlHjojAJ41GpNwmZVrokkaxN/61UMtkHv3MqKrKVatW0cXFhS4uLpw4cSK9\n9XpjfLwjQG8nJ5v5ypMMSZz/+3w6znCk22w3rjixggbV8lDRc2Hn6DHHg7U/r83wGOsnZ1lDrt0l\nqioWAH7uObNvUoMGDSIHDhRTSm2cVUxVVS47vozOM53pNtuN3/z1Dfus68NxO8bx9N3TbPV1K8If\nfGfXOxZH5hw8eJDeTk75dl88iHnA17a+RviDtT+vzcPXrZs38m/4v/Se683qi6tb9UZD0jhXYd2M\nGQTAPn36MNns9GuR4cTHR2SHGD5cGv6SRokw/hlm55rg9u3b7Natm3FCT2p65JiWLTkFYmbsVDs7\n4wzIvHLm3hk2W9mM8Ad7/tSTt6LM+1iz4/D1w9TP0PO5Vc8xOiFzYhzbcTniMmstqZXB6FscWrhg\nAQlwmLkkd507i9vD39+mOl+PvM7OqzsT/mCX1V14PdJ01NCEPRMIf3D+7/Mtlh0TFMQpWq1N7wtV\nVbnu7DqW+bQM7abZcWrgVMYl5S6s5tjNY3Sa6cTGyxvzcfzjnCukEhEhHPoTJ3LRokUEwNGjR2fb\nMIaGijl9gMms5pJiTLE3/tnN8FVVlT/88AM9PDzo6OjIJUuWpE3UOnJEqD16tMiBcsTyweLMpPrK\nQx+F8n+B/6PdNDuW+bQM159bn+dY8K0hW6kJ0LD72u5MTLZ9iOHx28dZ+/PaWXr9//n5P5YJCA8n\ndToOS5n4lMX4u7mJRXNtkN6aFP/Tr//6mm6z3eg805nLji/L9hobVAP7b+hP+IPrz623/IeOHLHZ\nfXHyzkm+/OPLhD/YbGUznr57OtcyU9l1cRe1AVp2Xt2ZCcmmF/sxyYsvitxMqspJkyYRAP2zaZiD\ngkQCUycn4QLatCnPqkuKCCXW+IeFhbFv374ifLNFC168mM7Xrqpk27ZihYssqSatZ+yOsVT8FXrM\n9iD8wde2vsYHMVasC5gDK06sIPzB4T8Pt9nEoiRDEqcdmEa7aXastKASW33diuN2jOOK4yuo+Cv0\nnedreWMzcCAPurrSO3OSOycnHgRIU+sQ5ILbj2+z+9ruxvGTKxGWrSkclxRnjKI6FHrIJrpYwpjt\nY4zjD44zHDn/9/lmB/pzw3envjNGCFnsUly1Sjyup05RVVW+/vrrBMAVK1ZkKZrex//nn2JSt52d\nWJ9YUvwpkcZ/8+bN9PX1pU6n49y5c7P6NXftEip/+WVurxtJ24QWWop/sD/hD07ZPyXPsi4+uMjn\nVj1H+INDNg9hRGzGyI9Uo/LGz29Y1tjs20cCjPn227SQ0//+lzEuLsYcSXlBVVX+cOYHes7xpOMM\nRy46usiq8RNS+Nprf16bnnM8GRIekid9cqIg74s5h+cQ/uD43eMt+1/dvy+68FOnkhShuN26daNG\no8myZnTmWdvbtgn/f506ciZwSaBEGf+IiAgOHTqUgFjiLsOiHakYDGTDhuLV18zauJay8+JOusxy\nMT7cjjMcrZ6GbymqqnLUtlGEP7jk2JJcy1h2fBmdZjrRc44n151dZ7ZsamPjH+yfs2CDgaxalezY\nMW3fsGGiq3gpF1Ep6bj35B77rOtD+IMtvmrBCw8u5FzJDFcjrrLMp2VYdVHVfPkfkeIaf3bkM9oF\n2BnvC6cZTvl6X/x3938Jf3Deb/Msq9Shg7DgKURHR7N58+bU6/U5Ji5culQ87WPGyFxAxZ1ib/y1\nWi2nTJnCzZs3s3z58rSzs2NAQID5Kfg//ijUzRzAbAVJhiRjTLnzTGcq/gr1M/S5mlVqDcmGZPZe\n15uKv8IN50ws3ZcNdx7fYbcfuhH+4AtrXshxEFpVVb7x8xuEP/jdqe9y/oGUZG28fJk8dEh8Tuld\nWqtn6lyDDec20GeeDx2mO3Deb/OsCtk0x/Hbx+k004lNVzTlk4QneZaXnvvR99l7XW/CHyw/v3yB\n3RcG1cCBGwcS/uDq01kXmcnCl1+K/8/588Zd4eHhrFWrFj08PHJcc3rSJFF9zpy8ai4pTIq98U8f\nU161alWePHnS/NkmJIgef8OGOS88a4b0LpOhm4fypR9fMoYWjtsxjn3W9cmVXEuJTYxl629aUzdd\nx6CrlkWhbDq/id5zvek4w5Ff/PGFxeMGCckJ7PR9J9pNs+P+K/uzL3zrlnAnfPgh2aAB6edH5mKm\n8dgdY6nx17DG4hrGAdLz98/nXNEKtl/YbhxEt5UPfufFnSz7aVnqpus4//f57L2ud4HeF/FJ8cb/\n1e5LOaTOuHNH+G+mTcuw+9q1ayxfvjwrVqyYbc4lg4EcNCjPfShJIVMijH/qNthEjvkMpPZ4cjHL\nVFVVrjixwiKXSX4TERvBp798mm6z3bKNHImMi+SrW141GtHc+Loj4yJZf2l9us1249/3/s6+8Esv\nCaMCGJd6tJSC9JOT5LLjywh/cNS2UXkaRI9OiOaY7WMIf/CZpc8U+Kzs9ETFR7Hx8sZ0munEP279\nkX3hVq1EI52JM2fO0M3NjXXr1s12xbf4eJEDyN6eDA7Om96SwqFEGf9sZ2FGR4vonrZtrXZW3nty\nzxiq13l151zH7duS1FnA5eaXMzkLOPhaMP0W+lEboOUnQZ/kKUz0RuQNVvisAisvqMzbj2+bLzhn\njrgVAJEfwIowyYsPLhoTmMEf1E+3Yq5BLpm8bzLhD848ZF1Dlcqft/5krc9rUfFX+P7e93Mdt29L\n7j65y2qLqtFnnk/24yOpq9WZGJMJDg6mTqdjixYtss0TFRFB1q0r1gQ2NbwmKdoUqPEH0BXABQCX\nAUw2U6Y9gNMAzgM4aIFMy4z/zJlCTSvjtrf9u42+83zpMN0hV1Em+cn5++fpOceTtT6vxbNhZ9n2\n27a89ugaJ+yZQMVfYc0lNXns5jGb/Napu6foMsuFjZY3Mj+xaMaMtJ5/ypqxlnAo9BCrLaqWobef\n335yUvjKh2weQviDa86ssbheapisNkDLygsqW+x+KyguPrhI33m+rLqoKk/dOWU6X9P168zOcb9x\n40YqisJu3bpx0qRJxnxNmRuD0FCx/LGfH3k7m36BpOhRYMYfgBbAFQDVAegAnAFQL1MZDwD/APBL\n+V7GArk5pzF+8IB0c7Mqt8yThCccuW0k4Q82Wt6I58KKZtfmt+u/UT9DzzKflqHir9BzjifhD47d\nMdbms4J3X9pNbYCWXX/oavpN4sgR0ePXai3q+ccnxfPDXz+k4q+w+uLqbPtN2wL1k6fqkJoxM/Bq\nYI7lLz28xOe/et445vMoLv/SPOeF47eP03mmM73nelPjb6YhffZZsZivGcaPH58hT5O55+zkSbGu\nfePGYnlISfGgII1/CwB7033/CMBHmcqMAzDDmh/WarU5pzH+4APRI7Xw3fTozaN8aslTVPwVTto3\nifFJVqwGVsAUtK985YmVhD84cttI075yC2fGnr57ms8sfcbod7d15I01PIp7ZBxDORtmOtJFVVWu\nPLGSzjOd6THHo1DHfCzBovsi1U1nZv1ha5Lc7dol2vyuXcl8XOtGYkMK0vj3B/BVuu+vAvgiU5lF\nAL4EcADASQCv5SQ3u/QOJMmbN0kHB/L113O8GInJifw46GNqAjSssrAKD4bm7/qxtuDO4zscsmkI\nddN1+T7XIJUp+6cQ/uCsQ5a5ddKTbEjm7MOzaT/NnmU/LcsdF3bkg4bWcz3yOsvPL89KCyplGdMJ\niw5jz596Ev5gx+878mbUzULS0nIy3xeaAA0HbRyU8b64dEk8ugsWmJRhbYbT1MnDI0bIOQDFAVsZ\nfw1sgx2ApgB6AHgRwMeKotTKXEhRlFGKopxQFOVEeHh49hIDAsQQZECAycN3n9xFu+/a4fcbv6PV\nN60w/dB0DGswDGfGnEHbKm3zfEL5TXnX8nBzcEOymgy9nR4JhgS4ObihnEu5fPvNGR1nYMgzQzAl\naAp+PPujxfWuRFxBu+/a4aPAj9CrTi+cG3cOPWr1yDc9rcHP3Q+7hu5CZHwkuv/YHRcfXkS779ph\n9enVeGbZM9h7eS8WvrgQ+17dh0pulQpb3RxJf1/Ya+yhUsUft/+Aj5NPWqGnngIaNgQ2bbLJb775\nJvC//wFffw3MnGkTkZLiQE6tAyxz+0wGEJDu+9cABmQnN9uef0iIiD0fP95skdT8K9oALb3melm1\nhGJRIX0a44L0lbf7th1103U8cO1AtmVTw2SdZzrTfbY715xZk2+Ln+eVPZf2UBugZaXPKhldJQ2X\nNTTrDirKmEpvPWTzkIyT5aZNE911E6O1mZeCTPX9v//++2Z/U1XJV18VIr//Pj/OSmIrUIBuHzsA\nVwFUQ9qA79OZytQFEJhS1gnAOQD1s5ObrfHv10+sSGFiIfaC9pWXRCJiI1jnizr0mOPBf+6bXvf4\n7pO77LG2B+EPdvq+E29E3ihgLa2jJN8Xsw7NIvzBN395M63x/ecf8fh+8YXJOumXgpw8eTJfeOEF\nKorCNWvMR0clJJCdOokkcPv25ceZSGxBgRl/8VvoDuAiRNTP1JR9YwCMSVdmIkTEzzkA43OSadb4\n//knzeWRTzIk0T/YP8OSf/mZf6UkczXiKst+WtZkvpyN5zfSe6439TP0XHxscZEKkzVHZl95Sbsv\npgZOJfzBd3e9m9YA1K1Ltm9vUf3Y2Fh26NCBGo2Gm7LJ7xwZSdavL9I7rVqV8ZhcCrJoUKDGPz82\ns8a/UyfS1zdL7Nlfd/5i0xVNMyz5V1Bx5SWV9PlyLj+8zJZft2S/9f3yNLO4MBmzfQw1AZoSeV+o\nqsr39rxH+IOT900WDcD//ifcoybekE3x5MkTtmzZkvb29tyZTX7nGzdESmiNhlyfspSCXAqy6FAy\njX9KimEuXmzcFZ0QzQ/2fkBtgJZlPy3LDec2FIqvvKSy7d9t1ARoWH5+eePblH+wf74sQJPflPT7\nQlVVjt4+mvAHpx+cTp4+LZ6XlSstlhEZGckmTZrQwcGBgYHm50ecOZM29eODD6ThL0rYyvgrQlbB\n06xZM544cSJth6oCzZsDDx4AFy4ADg7Ye3kvxu4ci2uR1zCyyUjM7TwXno6ehaJvScVxpiPik+Oz\n7Nfb6RE3Na4QNJJkh0oVb/z8Btb8vQYLXvgM7726VET/7NljsYwHDx6gffv2CA0Nxd69e9GqVSuT\n5fbtA7p2FY/mf/8LLFpkq7OQ5AVFUU6SbJZXObYK9cw7mzcDJ08C06bhfnIUhm0Zhq5ru0Kn1eHg\nGwex8uWV0vDnA1ffvYoh9YfAQesAAHCyc8LQZ4bi2n+vFbJmElNoFA2+6fUN+tfrjwm/vo8VA2sA\ngYHAo0cWy/Dx8cH+/ftRoUIFdO/eHRk6YemwswNcXQGdDvj8cxEKKik5FA3jn5QETJ0K1n8a39VP\nRt0v62LD+Q34v3b/V2zi9osrqXHlSWoS9HZ6xBvi832+gSRv2GnssLbvWvSo2QNjdfuwpl4ysG2b\nVTLKlSuHwMBAeHp64sUXX8TZs2czHA8OBl55Bdi6FTh+HPD0BEaOBBYutOWZSAqTomH8v/sOlx9e\nQufhWgzfPgJ1feri9JjT8G/vDwc7h8LWrsQTFhOGMU3H4NiIYxjTdAzuRd8rbJUkOaDT6rDplU3o\nWK0j3ugNbAz83GoZlStXRlBQEPR6Pbp06YKLFy8ajx0/DmzYAHToADRoAPz1F1C5MjBxIrB+vS3P\nRFJYFLrPP+lJFOYPqoxpTWOgc3LBvM7zMLLpSGiUotEuSSRFmZjEGLw4vRb+UO5ga9/1eKnRK1bL\n+Pfff9G2bVs4ODjg8OHDqFq1qslyERFA797A4cPAZ58BEybkUXlJrij2Pv8LDy9g18VdaLqwDqY0\nf4KXyrVFyFshGN1stDT8EomFOOucsbPzN2h0D+i/bRj2X91vtYw6depg3759iImJQceOHXH79m2T\n5by8gF9/Bfr1A95/Xxh/Vc3rGUgKi0Lr+SsVFGI0UPGJgqV3m6DnWtODThKJJAdUFQ9rVED7QXG4\n6pqMvcP2orVfa6vFHD9+HJ06dULFihVx8OBBlClTxmQ5gwF47z0xCPzKK8Dq1YCD9M4WGMW+55/K\nbVdiYJ2zOReUSCSm0Wjg3b0f9q9KRCWXiui+tjtO3DlhTH5o6RjOs88+i507d+L69evo0qULbt68\niSlTpsDT0xNTp05FbGwsAECrBRYvBubNE+MCXbsCkZH5eYKS/KBQe/5O/wH6xFXF/ICjMrpEIskL\nwcFAx4649dMKtA2bg8j4SHSu3hmbQzZjdNPRWNpjqcWi9u3bh+7du4MkdDod4uLi4OjoCCcnJ2zZ\nsgVt26ZF3/34I/DGG0Dt2sDu3UClop84tdhjq55/oRl/TQWFykhgdJ2hWDr4h0LRQSIpMSQnA+XL\nA126QF9vCxIMCVmKWDNxr0OHDjhw4ECW/cOGDcOaNWsy7AsMBPr0AdzdRQNQv36uzkBiIcXe7VM3\nHBjzl4J7kbcKSwWJpORgZydCcbZvx7UxIXip5kvGQ/YaewyqP8iqiXuVrOjCd+oEHDokxgJatwYO\nHrRKc0khUWjG3zEZ+HK3BlsiXywsFSSSkkW/fkB0NMofPYdKbpWgQIFG0SBJTcLey3sRGhma559I\n9ftnplEj4OhR8fLxwgtiLEBStCncAV+dDmjfvlBVkEhKDB07Ah4ewObNCIsJw9hmY/HXqL/Q7alu\niEuOQ8uvW+Ld3e/iScKTHEWNHDkS3t7ecHR0BADY29sDAPbs2YPly5dDNRHjWaUK8PvvwLPPAgMH\nAuPGZTweHCwGiSVFBFtkh8vN1rRixRwXC5dIJFby2mukh4dYmSUdUfFRfHvn21T8FVZeUJnbL2zP\nUVT6BWGmTp3Kv//+mx06dCAAtm7dmiEhplN+x8aSbdqIhKP9+5MGg0wJbUtQIlM6SySSvPHLL+Kx\n3rPH5OEjN46w3pf1CH9w4MaBvPfknlXiVVXlN998Q09PT+p0Ok6bNo0JmRoakkxOJnv1EqrUqUN6\neUnDbytsZfwLPc5fIpHYkBdeAFxcRJZcE7So3AKnRp9CQPsAbP13K+p+WRffnvpW9AQtQFEUDB8+\nHCEhIejTpw8++eQTNGnSBEePHs1QTqsVSeE6dQL+/VckHV29WnyWFA2k8ZdIShJ6PfDSS8DPP4vw\nTxPotDp80u4TnB59Gk+XeRr/2fYfdF7TGZcjLlv8M2XLlsW6deuwfft2REVFoVWrVnjnnXfw5Ena\neMKBA8CZM8C774oZwGvXAvXqAf37i+ztkkLGFq8Pudmk20ciySc2bhT+Fgv8LAbVwOXHl9Ntthv1\nM/ScfXi21au4PX78mO+88w4VRWGlSpW4bdu2LD7+oCDS25scMoR0dxfqvfACeeAAmbokscQyIN0+\nEonEJN26AY6OZl0/6dEoGoxuNhr/jPsH3Z7qho8CP8Kzq57FiTsi15YlKSJcXV2xZMkSHDlyBO7u\n7ujZsyfGj1+LRYvuYN8+kR5i//6pWLMmHg0bAtevA7NnA6dPi2C/1q2BHTsACz1PElthixYkN5vs\n+Usk+UjfvmT58iLUxgo2/7OZ5eeXpyZAwwl7JvDNX96kJkDDsTvGWlQ/ISGB06dPp52dHRVFoU6n\nIwA6OjrS29ubBw8eNJaNjSW/+IL08xNvAg0akD/+SCYlWaVyqQMlbg1fiURiO378ERg6FBg9Gnj9\ndaBFC4urRsZHwmeeDww0ZDlmaYqInj17Yvv27Vn2m0oPkZQE/PQTMGcOEBIC1KgBfPihWM67RQux\noEwqwcFioZkPP7T4dEocxT69g0QiyUdS0zGvXClCbjJF42SHh94DN9+7ic7VOkOTzkTo7fToXbs3\ntoZszXGimLu7u8n927dvx6RJk3Do0CEkpwxI29sDr70GnDsHbNkilowcPRpYsAB4+WVg1y5RN3Vp\nyWeftfhUJNkgjb9EUhI5flz8JYHERBF6YwXlXcvjKa+nAAVw0DpAgYIKrhWw6/Iu9N3QF97zvNFl\nTRcsOrYIlx5esliuXq/HggUL0K5dO/j6+mLQoEH44Ycf8ODBA2g0IkHcn38C+/YBDRsCMTEieKl5\nc6BXL2D+fKBdO6tORWIG6faRSEoiR4+K0dTERBFnGRxslesHAPqu74vyLuUxqukorDy5Enej72J9\n//X4/ebv2HlxJ3Ze2omQByEAgJpeNdGjZg/0qNUDbau0xbHfj6Fv376IUWIQ/1I89Dv0cKYztmzZ\ngkaNGmHfvn3YuXMndu7cifv370NRFDz//PPo0aMHevTogYYNG0JRFBw4EId+/Z4gIiJtYRlXV6Bp\nU6BZs7StenVAUWx5AYsuxT6lszT+Ekk+ExQkVlrp3l3E/ecD1x5dw85LoiEIvhaMBEMCXHWu6FKj\nCzpX6YxlO5fhrOYsmqhNcHjyYTg5OWWor6oqTp48aWwIUm1CxYoV0bhxYwQGqoiL+w7ACgBvQa9f\njW7d+uPOnYo4fRpISMlc7eGRsTFo1gzw8wM+/VS4iUrSuIE0/hKJJGeGDBEL7969K5zr+UhMYgyC\nrgVhx8UdWPnXSpNl7DR2WNt3Lfzc/eDn7odyLuUyrNl979497N69Gzt37sTWrZFQ1Z8Al15A/6PA\nphZA9C+oVetjfPppd5Qv74eYmGq4eNENJ08qOHEC+PvvtLltPj5AtWrAuXNEs2b7cPr0dHTuPA6H\nDg3Cxo1KhgYhO+bNy1sDktf6mWVI4y+RSHJm2zbhLN+1S8T/FxB3Ht/Bm9vfxP6r+5GkJkGBAq2i\nRTIzzjq219ijklslY2OQfhs//G9c+HM70OU3oCmAkwB2tgfwLIBPjTKcnZ3h5+cHPz8/lC9fDQ4O\nTREXVx/h4X44f94RN264AS73gf6DgE3rgehycHRMRrlydvDxgcnN1zft87//AiNHEm27f4ZtDpPR\nM2EuDu+egA0bLGtAgoOBAQNyXz+zjK27JpIPmOfxWru8CpBIJEWYF18UPpF16wrU+Fdwq4Aq7lVg\noAF6Oz0SDYkY2XQkZneajRtRNzJuj8Xfg9cP4vbj22khpp1TtlSeBfDsAYAH0LF8R6jxKpJikhD/\nJB5xUXH45+E/OHL9CJ48XA4kQmwEUNYBaNUQ8DsOdBoK7HsGir4akjV1cDvMG6E3vJAQ7474OBck\nJppbiZ7YGnEFaEpsPXkJysObGDnSHd7eLtDrFej1ChwdAQcHBXq9GGZJ/RsWdgNPnhzG1ogLQFMV\nW0+GQB+9Ajt3dsTVq7Wg0YhcSKmbqe///HMWsbGbsDXiFqCDTUY3ZM9fIinpjBgBbNwIhIWJmb8F\nhKkB4y0Dt2RbJ1lNxr3oe7gRdQObDm/Ckt+XwOBtALQAVECJU1C9THXoHHWISYpBdGI0YhJjTC5b\naTVqymZQAFWTsmkBl3iYNLcEcL8qQC0AjfhLDUC7dN+1QMXjgMaEnVU1wPWU0CWm/oBi+nu1QECT\nsobCCoB3mOcGQBp/iaSks38/0KWLSPfQt29ha2MVb/78Jr45/Q1oIBStghGNR2BVr1VZyiWryYhJ\njDuad1UAABs6SURBVEFMUgxiElMahaQYjJ30Oc45Xgcq/gnYEUhWgLCGqJ7sjbFvdkWymmzc4pPi\nERsfi/iEeMQnxiMuMQ4JiQn44/QfuG8fBXgkAxqDaBAe28Et3gF+Ff2gqipUqjDQAFVVQRIqVeP2\nIPIB4p0MgLNBGHBVA8TawT7GDi6ObiAAULQlqeY41SqTAEHEJcZBdUkAHBOBVbSJ8ZduH4mkpNO+\nvZj09dNPxc74RyREYOyzYzO8PZjCTmMHd7073PUZJ5e1cq+AC/c7IakygSQAWsI+PBJdym7GBy2r\nW6RDly6zsN/hItD0+xQZBuDSq2ieUBP7lk3JRX0VCBmCdgk1sW9fzvWzyLBRf10af4mkpGNnJ6bG\nfvUV8OSJCJQvJqR3E33Z40ur6y9fXh13f2yAW/964dL6S6g1uBYqDqiI5UMsM/wAUKPGIByIbg2e\n1sLwhwHa57RQPPaihsvHBVI/i4wHWdNu5ApbJAjKzSYTu0kkBchvv4nsaWvWFLYmxZLMS1rGxMQU\naP30MgAks6ASuymK0hXAYohhl69IzjFT7lkARwEMIrkpO5nS5y+RFCCqKoLen3lG5E+WFFsKLLGb\noihaAF8C6AagHoDBiqLUM1NuLoBf86qURCKxMRoNMHAgsHcv8PBhYWsjKQJY4vNvDuAyyasAoCjK\nOgC9APyTqdw7ADZDROPmiqSkJNy6dQvx8fG5FVHi0ev1qFSpEuzzebampAQyeLDId7BlCzByZGFr\nIylkLDH+FQHcTPf9FoDn0hdQFKUigD4AOiAPxv/WrVtwdXVF1apVoZSWLE1WQBIPHz7ErVu3UK1a\ntcJWR1LcaNQIqFVLRP1I41/qsVVK50UAJpFUsyukKMooRVFOKIpyIjw8PMvx+Ph4eHt7S8NvBkVR\n4O3tLd+MJLlDUUTv/8ABketHUqqxxPjfBlA53fdKKfvS0wzAOkVRQgH0B7BUUZTemQWRXEmyGclm\nvr6+Jn9MGv7skddHkicGDRIzhzZsKGxNJIWMJcb/OICaiqJUUxRFB2AQgG3pC5CsRrIqyaoANgEY\nRzJ/cshKJJLcU6eOcP+sW1fYmkgKmRyNP8lkAG8D2AsgBMAGkucVRRmjKMqY/FZQIpHYmEGDgGPH\ngGvXClsTSSFikc+f5C6StUjWIDkzZd9ykstNlH0jpxj/ooxWq0WjRo1Qv359DBgwALGxsQAAFxcX\ns3VCQ0Px448/FpSKEkneGDhQ/F2/vnD1kBQqxX8N36NHgdmzrVqgOjscHR1x+vRpnDt3DjqdDsuX\nZ2nfsiCNv6RYUbWqWNLxp58KWxNJIVJ0c/uMHw+cPp19magosXSPqopJLA0aAO7u5ss3agQsWmSx\nCm3atMHff/+dY7nJkycjJCQEjRo1wuuvv4733nvP4t+QSAqFwYOBd98F/vkHqJdlzqakFFC8e/5R\nUcLwA+JvVJTNRCcnJ2P37t145plnciw7Z84ctGnTBqdPn5aGX1I8GDBAdJjkwG+ppej2/C3poR89\nCnTqBCQmAjodsHateJ3NA3FxcWjUqBEA0fMfMWJEnuRJJEWScuXEgrDr1gEBAWIOgKRUUXSNvyW0\naAEEBopJK+3b59nwA2k+f4mkxDNokJjp+9dfQNOmha2NpIAp3m4fQBj8jz6yieHPLa6urnjy5Emh\n/b5Ekiv69gXs7aXrp5RS/I1/EaBBgwbQarVo2LAhFi5cWNjqSCSW4eUlFnhfty5t7ExSaijebp98\nIDo62qr9AGBvb4+goKD8UkkiyT8GDxb5/Y8cAVq3LmxtJAWI7PlLJKWZnj0BR0cZ818KkT1/Kzh7\n9ixeffXVDPscHBzwxx9/FJJGEkkecXEBXn4Z2LgRWLxYrPcrKRXI/7QVPPPMMzISSFLyGDRIZPkM\nCgJeeKGwtZEUENLtI5GUdrp1A9zcZNRPKUMaf4mktKPXA336iOUdExIKWxtJASGNv0QiEVE/UVHA\nnj2FrYmkgJDGPxPmUjpbys8//4x//sm8tr1EUsTp2BHw8ZFRP6WIYm38Y2JiMGXKFHh6emLq1KlW\nG2pT5Calc3qk8ZcUS+ztRbK37duBmJjC1kZSABRb43/o0CFUqVIFixcvRmRkJBYuXAg/Pz8cOnTI\nZr/Rpk0bXL58GQCwevVqNGjQAA0bNswS7pnKkSNHsG3bNkycOBGNGjXClStXbKaLRJLvDBoExMYC\n27blXFZS7CmyoZ7jx4/PNqwyJCQEDx8+NH6Pi4tDXFwcBgwYgLp165qs06hRIyyyMJ9/akrnrl27\n4vz585gxYwaOHDkCHx8fREREmKzTsmVL9OzZEy+99BL69+9v0e9IJEWG1q2BihWF62fw4MLWRpLP\nFNuef36RmtK5WbNm8PPzw4gRIxAUFIQBAwbAx8cHAODl5VXIWkok+YBGI5Z43LMHePT/7d19cFT1\nucDx75MXIBIuL8EBAoTXXOkFQoLhJUzNC1ob5QoVtaAUa0VTOmIF6lhoRIOaFgUrtCgIF4cXmauA\neHEcUG8LucjIa51IiYBBDAEKccAX3jQQ87t//DbJJuwmS7K7Zzd5PjM7u3vO2T1Pfhyec/Z3zu85\nXzsdjQqwkD3yb+gIffLkybz++utXTb/11ltZs2ZNo9erJZ1Vi3bvvfDnP9vLPvVeFs1a2B75P/zw\nw8TFxRETEwPYpB0XF8fDDz/s93WNHj2a9evXV3czeev2AS3vrMLcjTdCv3464KsFCNvkn56eTmlp\nKTNmzKBDhw7MnDmT0tJS0tPT/b6ugQMHkpubS0ZGBkOGDGHmzJlel504cSLz588nJSVFT/iq8CNi\nj/63boXTp52ORgWQGGMcWXFqaqrZt29frWkHDx70erJW1dB2UgFVVASDBsFf/wrTpjkdjapDRP5h\njElt6veE7ZG/UipABg6EwYN1wFczp8m/kfLz80lOTq71yM/Pdzospfxj4kR7g5djx5yORAWIdvuE\nIW0nFXBHj9oTv88/D0884XQ0yo2/un1C9lJPpZSD+vaF4cNhxQr44QfIzIS0NKejUn6k3T5KKc/S\n0uCzz+DJJ+Hmm2HnTqcjUn6kyV8p5dl119nnykq4fBkKChwNR/mXJv8Q9cc//tHpEFRLd8cdNff0\njYqyXT+q2dDk7yBjDJWVlR7nafJXjktLg/ffh3bt7DmAESOcjkj5UVgn/7K1ZezsvZOCiAJ29t5J\n2dqyJn9nSUkJgwYNqn6/YMEC8vLyyMzM5LHHHqu+0cuePXsAyMvLY/LkyaSlpZGYmMjy5curPzt/\n/nyGDRtGUlISTz/9dPX333DDDdx///0MGjSI48ePXxXDrFmzqgvMTZo0qcl/k1KNNno0/OUvcPCg\nlnxoZsL2ap+ytWUczjlM5SV75Fx+rJzDOYcB6DKpS0DWeenSJQoLC9m+fTsPPvggBw4cAGD//v3s\n2rWLixcvkpKSwpgxYzhw4ADFxcXs2bMHYwxjx45l+/btJCQkUFxczKpVqxg5cqTH9cybN4/Fixdr\ngTkVGu6/3472nTULfvazmnMBKqyFbPIvnl7MhcILXuef23UOU157jELlpUoOTTnEv5b/y+NnYpNj\nSVyY2OiY7nXVOE9PT+fcuXN88803AIwbN46YmBhiYmLIyspiz5497Nixgw8++ICUlBQALly4QHFx\nMQkJCfTq1ctr4lcq5EREwEsvQUaGrfj55JNOR6T8wKduHxHJFpHDInJERGZ5mD9JRPaLyD9F5CMR\nGeL/UGurm/gbmu6rqKioWv3w33//ffVrEam1bNV7T9ONMcyePZvCwkIKCws5cuQIU1wlctu2bduk\nGJUKuvR0GD8e5s2Df3k+uFLhpcEjfxGJBF4GfgKcAPaKyDvGGPcb1X4BZBhjvhaR24BlQJPODjV0\nhL6z907Kj5VfNb11r9akFKQ0er1dunThyy+/5OzZs8TGxvLuu++SnZ0NwJtvvklWVhY7duygffv2\ntG/fHoBNmzYxe/ZsLl68SEFBAfPmzSMmJoY5c+YwadIkYmNjOXnyJNHR0T7HER0dzZUrV67pM0oF\n1Asv2Hv8PvkkvPaa09GoJvKl22c4cMQYcxRARN4AxgHVyd8Y85Hb8ruAHv4M0pO++X1r9fkDRFwX\nQd/8vk363ujoaJ566imGDx9O9+7dGTBgQPW8Nm3akJKSwpUrV3jNbeNPSkoiKyuLM2fOMGfOHOLj\n44mPj+fgwYOkuUZFxsbG8vrrrxMZGelTHDk5OSQlJTF06FDWrl3bpL9JKb/o1w9++1vb9fPoo5DS\n+IMs5bwGa/uIyN1AtjHmIdf7ycAIY4zHWq8i8jgwoGp5b/xR26dsbRlHc49SXlpO64TW9M3vG7CT\nvZmZmSxYsIDU1NolNfLy8oiNjeXxxx8PyHo90do+yjHffAOJibbk89attv6/CqqQrO0jIlnAFODH\nXubnADkACQkJTV5fl0ldApbslVIedOgAc+fCI4/Apk326h8Vlnw58k8D8owxP3W9nw1gjPlTneWS\ngLeB24wxnzW0Yq3qaY0YMYLy8trnLtasWcPgwYO9fqYltpMKIRUVMGSILflQVAStWjkdUYsSzCP/\nvUCiiPQBTgITgfvqBJMAbAQm+5L4VY3du3c7HYJS1yYqCl58EW67DRYvhnpua6pCV4OXehpjKoBp\nwPvAQWCdMaZIRKaKyFTXYk8BccArIlIoIvu8fJ1SqjnIzoaf/hSeeQbOnHE6GtUIPvX5G2M2A5vr\nTFvq9vohoN4TvEqpZubFF233z9y5dgSwCithXdtHKeWggQMhJweWLLG1f1RY0eSvlGq8uXMhNhaC\neKmz8g9N/kqpxrv+ejvid/Nm+OADp6NR1yBsk/8LL8C2bbWnbdtmpzdFZGRkddnme+65h0uXLl3T\n57dv387QoUOJiopiw4YNTQtGqXDw6KO23v/MmfYyUBUWwjb5DxsGP/95zQ5g2zb7ftiwpn1vTEwM\nhYWFHDhwgFatWrF06dKGP+QmISGBlStXct999zW8sFLNQevW9qirqMje8F2FhZAt6Tx9OjRUzj4+\n3l5t1q0bnDoFP/qR7YKcO9fz8snJsHCh7zHcdNNN7N+/H4DVq1ezYMECRISkpCTWrFnj8TO9e/cG\nICIibPerSl278eNt5c85c2DiRHAVPVShK6wzVMeONvGXltrnjh39990VFRVs2bKFwYMHU1RUxHPP\nPcfWrVv55JNPWLRokf9WpFRzIGILvp05A3oL0rAQskf+vhyhV3X1zJljrzZ7+mnIymraeqtunwj2\nyH/KlCm8+uqr3HPPPXTu3BmATp06NW0lSjVHN95o7/q1cCH8+tf2PIAKWSGb/BtSlfjXrbMJPyur\n9vvGqurzV0o1Qn4+rF9vb/m4bp3T0ah6hG23z969tRN9VpZ9v3ev/9c1evRo1q9fz9mzZwH46quv\n/L8SpZqD7t3hiSfsDmDHDqejUfVosKpnoIRqVc/Y2FguXLj63sGrVq1i/vz5REZGkpKSwsqVKz1+\nfu/evdx55518/fXXtGnThq5du1JUVOTXGEOhnZTy6uJFuOEGeyJu9257D2DlN/6q6qnJPwxpO6mQ\nt2aN7f9fvRomT3Y6mmbFX8lfd8lKKf+bNAlSU2H2bPtLQIUcTf6NlJ+fT3Jycq1Hfn6+02EpFRoi\nIuCll+DkSRg3DnbudDoiVUfYXu3jtNzcXHJzc50OQ6nQFRlpH3//O3z4IRQUQFqa01EpFz3yV0oF\nRkFBzevLl20BuMpKx8JRtWnyV0oFRmamvb9v1S+ArVvhrrvg/HmnI1Not49SKlDS0myXT0EBZGTY\nQTi/+52dvmkT9OvndIQtmiZ/pVTgpKXV9POPGmXv/lVVfnfdOrjlFmfja8HCvtvn1PlTZKzM4PSF\n0375vqbW8y8vL2fChAn079+fESNGUFJS4pe4lGoWbrnF/gKoKsm7cCE4NNaopQv75P/s9mfZUbqD\nZ/7vGb98X1Pr+a9YsYKOHTty5MgRZsyYwe9//3u/xKVUs9Gvn730c9w4mDEDfvUr+P57p6NqcUK2\n22f6e9MpPO29wNqHpR9SaWquHFiybwlL9i0hQiK4KeEmj59J7prMwmzfC/o3pp7/pk2byMvLA+Du\nu+9m2rRpGGMQEZ/Xq1Sz164dbNgAzz4LeXn2BvBvv21/EaigCNnk35Dh8cM5+vVRznx3hkpTSYRE\n0Pm6zvTr6J+TSFX1/LOzs6vr+X/00Ud07ty53sJuJ0+epGfPngBERUXRvn17zp49W10OWinlEhFh\n67AnJdkSEKmpsHEjjBzpdGQtQsgmf1+O0H/z7m9Y9vEy2kS14fIPl7nrR3fxyphXmrRereevVJDd\neWdNN1BGBrz6KjzwgNNRNXshm/x9UXaxjKk3TiXnxhyW/WMZpy6cavJ3NrWef/fu3Tl+/Dg9evSg\noqKCb7/9lri4uCbHpVSzNniwPRE8YYI9B1BYCAsWQFRYp6iQFtYnfDdO2MjLY15mSNchvDzmZTZO\n2BiQ9VxLPf+xY8eyatUqADZs2MDo0aO1v18pX8TFwXvvwWOPwaJFkJ0Nrv9zyv90t+qDgQMHkpub\nS0ZGRoP1/KdMmcLkyZPp378/nTp14o033ghusEqFs6goe/nnkCEwdSoMHw5z58Lx43bEsNYG8hut\n5x+GtJ1Ui7BrF4wZA199ZW8Q36aNHTHcwncAWs9fKdW8jRwJOTn2tTHw3Xf2RPCKFXDmjKOhNQea\n/BtJ6/krFQRjx0JMjL0sNCoKzp2Dhx6Crl3h5pvhlVfgVNMv9GiJtNsnDGk7qRZl505bHC4z0/4a\n+PhjOx7grbfg8GHbJTRqlK0YetddkJDgdMQB1Wzv4TtgwAC9OqYexhgOHTqkyV8pY+DTT+1O4K23\nwDUan9TUmh1BYqKzMQZAs0z+X3zxBe3atSMuLk53AB4YYzh79iznz5+nT58+ToejVGgpLq75RbB3\nr502eLDdCfTrB6WlkJUV9ieMm2Xyv3LlCidOnOB7LfLkVZs2bejRowfR0dFOh6JU6CotrdkR7NhR\nM13EXj6anAy9e0OvXjWPbt3suYUQF9TkLyLZwCIgEvgvY8y8OvPFNf924BLwgDHm4/q+01PyV0op\nv/vDH2DevJrS0fHxUF5+9QCyVq2gZ8+anUHdncOJE3ZH4vB4A38l/wYHeYlIJPAy8BPgBLBXRN4x\nxnzqtthtQKLrMQJY4npWSiln3XGHHTh2+bJN8Bs22OR94QIcO1b7UVJin997r/6riOLj7Yjk2Fho\n29a359JSW700NRVSUuzVS+6P6Oj6p0VEwK5ddIeu/mgWX0b4DgeOGGOOAojIG8A4wD35jwNWG/sz\nYpeIdBCRbsYYvQZLKeUs99tJuh+1x8baO4sNHOj5c+XldmRxSQksWWJLThtju46uvx769LE7kIsX\noazMPle9v3jRezzLlzfpz+kK3Zv0BS6+JP/uwHG39ye4+qje0zLdAU3+Sinnud9O0letW0P//vbR\nti1s2VLz62HJkvq/r7LSDkqr2hksWgSLF9vpERHwi1/A+PFQUQFXrtjnuo+60wsKYNs2v935LKi1\nfUQkB3AN2aNcRA4Ec/2N1BkIh+GEGqd/hUOc4RAjNJM420Hbf4N257777vz5UaPqObT3/NlE+HdA\nqKw0xatXf3Z+9epGfccxPw3O9SX5nwR6ur3v4Zp2rctgjFkGLAMQkX3+OGkRaBqnf2mc/hMOMYLG\n6W8i4pcrZXzZg+wFEkWkj4i0AiYC79RZ5h3gfrFGAt9qf79SSoWuBo/8jTEVIjINeB97qedrxpgi\nEZnqmr8U2Iy9zPMI9lLPXwUuZKWUUk3lU5+/MWYzNsG7T1vq9toAj1zjupdd4/JO0Tj9S+P0n3CI\nETROf/NLnI6N8FVKKeWc0B/LrJRSyu8CnvxFJFtEDovIERGZ5WG+iMhfXPP3i8jQQMfkIYaeIrJN\nRD4VkSIReczDMpki8q2IFLoeTwU7TlccJSLyT1cMV531D5H2vMGtnQpF5JyITK+zjCPtKSKviciX\n7pcZi0gnEflfESl2PXf08tl6t+UAxzhfRA65/k3fFpEOXj5b7/YRhDjzROSk27/r7V4+G5S2rCfO\nN91iLBGRQi+fDWZ7esxDAds+jTEBe2BPEH8O9AVaAZ8A/1FnmduBLYAAI4HdgYzJS5zdgKGu1+2A\nzzzEmQm8G+zYPMRaAnSuZ77j7elhGzgN9AqF9gTSgaHAAbdpLwCzXK9nAc97+Tvq3ZYDHOOtQJTr\n9fOeYvRl+whCnHnA4z5sE0FpS29x1pn/IvBUCLSnxzwUqO0z0Ef+1aUhjDGXgarSEO6qS0MYY3YB\nHUSkW4DjqsUYc8q4CtEZY84DB/HTEGoHON6eddwMfG6MOeZgDNWMMduBr+pMHgescr1eBfzMw0d9\n2ZYDFqMx5gNjTIXr7S7sWBpHeWlLXwStLaH+OEVEgJ8D/x2o9fuqnjwUkO0z0MnfW9mHa10maESk\nN5AC7PYwe5TrZ/cWEfFSECTgDPA3EfmH2BHTdYVUe2LHhXj7jxUK7QnQxdSMSzkNdPGwTCi164PY\nX3eeNLR9BMOjrn/X17x0UYRSW94ElBljir3Md6Q96+ShgGyfesLXjYjEAm8B040x5+rM/hhIMMYk\nAX8F/ifY8bn82BiTjK2k+oiIpDsUR4PEDgocC6z3MDtU2rMWY39Dh+wlcCKSC1QAa70s4vT2sQTb\n9ZCMre31YpDXf63upf6j/qC3Z315yJ/bZ6CTv99KQwSaiERjG3ytMWZj3fnGmHPGmAuu15uBaBHp\nHOQwMcacdD1/CbyN/bnnLiTa0+U24GNjTFndGaHSni5lVV1jrucvPSzjeLuKyAPAfwKTXEngKj5s\nHwFljCkzxvxgjKkElntZv+NtCSAiUcB44E1vywS7Pb3koYBsn4FO/mFRGsLV77cCOGiM+bOXZbq6\nlkNEhmPb7qynZQNFRNqKSLuq19iTgHWL4znenm68HlWFQnu6eQf4pev1L4FNHpbxZVsOGLE3VHoC\nGGuMueRlGV+2j4Cqc37pTi/rd7Qt3dwCHDLGnPA0M9jtWU8eCsz2GYQz2Ldjz1p/DuS6pk0Fprpe\nC/ZmMZ8D/wRSAx2Thxh/jP0ptR8odD1urxPnNKAIexZ9FzDKgTj7utb/iSuWkGxPVxxtscm8vds0\nx9sTuzM6BVzB9otOAeKAvwPFwN+ATq5l44HN9W3LQYzxCLZPt2r7XFo3Rm/bR5DjXOPa7vZjk083\nJ9vSW5yu6Surtke3ZZ1sT295KCDbp47wVUqpFkhP+CqlVAukyV8ppVogTf5KKdUCafJXSqkWSJO/\nUkq1QJr8lVKqBdLkr5RSLZAmf6WUaoH+H59yOIfUqhmYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1197f2160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18703)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21\n",
      "[1 1 1 1 1 1 1 0 1 1 0 0 0 1 0 0 1 0 0 0 0]\n",
      "stability stop at here 9 th problem\n",
      "similarity stop at here 10 th problem\n"
     ]
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAX8AAAD8CAYAAACfF6SlAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1185e9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "s2=final[(final['kc']=='egv_cc_9u11') & (final['s_id']==18706)]\n",
    "print(len(s2.correct))\n",
    "print(s2.correct.values)\n",
    "[prev, pred, upper, C1, C0]=process(s2.correct.values, 0.517, 0.00255, 0.434, 0.205)\n",
    "plot(prev, pred, upper, C1, C0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [default]",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
